ABSTRACT:Wood, T, Creaby, MW, Nicholson, V, Cowley, N, Hebron, I, Schultz, L, Timmins, R, Brennan, TR, and Weakley, J. Criterion validity and between-day reliability of three-dimensional motion capture and the perch camera system for monitoring velocity during weightlifting derivatives. J Strength Cond Res 40(5): e445-e453, 2026-Monitoring bar velocity in resistance training is widespread and can be beneficial for performance, but it is vital to consider the validity and reliability of the technology used. Consequently, this study aimed to quantify the criterion validity and between-day reliability of the Perch device across 7 weightlifting derivatives. Following 2 sessions to determine 1 repetition maximum (1RMs) of the power snatch, hang power clean, hang power snatch, clean pull, snatch pull, hang clean pull, and hang snatch pull, 14 subjects completed repetitions at 20, 40, 60, 80, 90, and 100% of 1RM across 4 subsequent sessions under a 3-dimensional (3D) motion capture system. To evaluate criterion validity, R-squared and root mean square error were calculated with generalized estimating equations, and Bland-Altman plots assessed the magnitude of difference between measures. Between-day reliability was calculated with SEM and minimum detectable changes (MDC) for 3D motion capture and the Perch across repeated sessions. The Perch explained 86-96% of the variation in the criterion measure, showing nearly perfect linear relationships, but was consistently ∼0.25 m·s-1 slower than the criterion, suggesting a systematic difference between the 2 devices. The SEM ranged from 0.04 to 0.19 m·s-1 for 3D motion capture and 0.03-0.16 m·s-1 for the Perch. The MDCs ranged from 0.10 m·s-1 to 0.54 m·s-1 for 3D motion capture and 0.09 m·s-1 to 0.48 m·s-1 for the Perch. The Perch device can be used to reliably monitor training and provide feedback, which can support performance and physical adaptation.

Advanced resistance training methods are commonly promoted as superior for long-term improvements in physical qualities and performance capacities. However, at present, there is no clear evidence that advanced resistance training methods are better than traditional approaches, or than one another, in promoting adaptation in healthy adults. This systematic review and Bayesian network meta-analysis aimed to (1) compare advanced methods of resistance training prescription and their effects on strength, power, hypertrophy, and performance adaptations in healthy adults; (2) identify variables that may influence adaptations following specific resistance training methods; and (3) provide a rank order of advanced resistance training methods in their effectiveness for developing each physical capacity. This review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension statement for network meta-analyses (PRISMA-NMA). Five databases were searched, with studies included if they were peer-reviewed investigations, written in English, and compared at least two of eight resistance training methods (i.e. traditional resistance training or one of seven advanced methods). Furthermore, studies must have assessed strength, power, hypertrophy, jump, or sprint adaptations. Risk of bias was assessed using the Cochrane Risk of Bias tool V.2. Bayesian network meta-analyses and meta-regressions were performed to quantify the differences between resistance training methods and identify any variables that may moderate adaptations. Strength and power adaptations were similar across all resistance training methods, with all relative effects from Bayesian network meta-analyses having 95

Understanding the acute demands of short-bout high-intensity interval training can enhance training outcomes. We aimed to examine the acute physiological and perceptual demands of short-bout high-intensity interval training in athletes and identify how they are moderated by programming variables, fitness level and competitive level. We searched the databases PubMed, SPORTDiscus and CINAHL on 2 December, 2025 for original research articles investigating running-based, short-bout high-intensity interval training in healthy athletes, aged 16–40 years, of any sex, who were recreationally active or above. Outcomes were analysed using a multi-level mixed-effects meta-analysis. The analysed outcomes were: average heart rate (HRavg), peak heart rate (HRpeak), peak and average oxygen consumption (VO2), time > 90

ABSTRACT:Chiang, YC, Lin, WC, Weakley, J, and Chiang, CY. Variable resistance training improves velocity and power output by reducing concentric deceleration in the back squat. J Strength Cond Res 40(7): e663-e669, 2026-Free-weight resistance training (FWT) presents constant resistance, potentially causing a sticking region that negatively affects velocity and power outputs. We aimed to compare FWT with 15, 25, and 35% variable resistance training (VRT) using elastic bands on back squat kinetics and kinematics in the concentric phase. Twenty resistance-trained men performed back squats at 90% of their one-repetition maximum across the 4 conditions in a counterbalanced order in a single session. Resistance in VRT conditions was normalized to ensure the average resistance was equivalent to the FWT condition. Kinetic and kinematic data in the concentric phase were recorded using force plates and linear position transducers. One-way repeated-measures analysis of variance with Bonferroni post hoc comparisons compared dependent variables between conditions. Significance was set at p ≤ 0.05. No significant differences were found between conditions for peak velocity, peak power, peak force, or mean force in the concentric phase. However, a dose-response relationship was observed for other variables. Increasing the VRT percentage led to a significant reduction in sticking region duration, particularly at 25% and 35%VRT, and a significant decrease in force at zero velocity. Conversely, mean concentric velocity, velocity at sticking region onset, and mean concentric power all increased with higher VRT percentages. In conclusion, VRT enhances back squat concentric mean velocity and power output by reducing force at zero velocity and increasing velocity at sticking region onset, representing an effective alternative to FWT for improving these specific performance characteristics.

ABSTRACT:Weakley, J, Murphy, S, Kan, M, Thurlow, F, Mitchell, L, Whelan, B, Williams, P, King, AJ, and Read, DB. A 10-year analysis of the Bronco fitness test in male and female Super Rugby athletes: The effect of sex and positional group. J Strength Cond Res 40(5): e479-e484, 2026-The Bronco fitness test is a commonly used assessment of high-intensity running capacity. This study aimed to (a) analyze 10 years of Bronco fitness testing data from male and female Super Rugby athletes by sex and positional group and (b) develop practical percentile thresholds. Bronco test data were retrospectively analyzed from 462 elite rugby athletes (303 males, 159 females), totaling 1,668 observations. Athletes were grouped by sex and positional groups. Linear mixed-effects models were used to assess differences, with Cohen's d effect sizes calculated. Percentile thresholds were derived from model outputs. Male athletes completed the Bronco significantly faster than female athletes (5:11 ± 00:02 vs. 6:19 ± 00:03 minutes:secs; p < 0.001; d = 1.78). Backs outperformed forwards in both sexes (male: 4:57 ± 00:02 vs. 5:21 ± 00:02; d = 1.09; female: 5:52 ± 00:06 vs. 6:38 ± 00:05; d = 0.98). Among male athletes, halves were the fastest positional group, while the Back 3 was fastest among female athletes. Front rowers consistently recorded the slowest times. Greater performance variability was observed in the female cohort, potentially reflecting the continued development of the female game. These findings provide normative data and percentile thresholds to guide profiling, conditioning, and long-term athlete development. Practitioners are encouraged to use these benchmarks to inform position-specific training and performance targets. The percentile tables and positional benchmarks presented can support practitioners and sport scientists in evaluating high-intensity running capacity, setting development goals, and tailoring conditioning programs for male and female Super Rugby athletes.

This study aimed to validate the feasibility of a repetition-based method for adjusting inter-set rest intervals during superset resistance training (RT). Twenty young adults completed three protocols-fixed (3-min rest), self-selected (participant-determined rest), and repetition-based (rest adjusted by prior repetition count)-in randomized order, performing five agonist-antagonist supersets (bench press and bench pull) at 75% 1RM. Results indicated greater repetition volume in later supersets for the self-selected and repetition-based protocols compared to the fixed protocol (F = 2.1, p = 0.037, omega p2 = 0.056). However, blood lactate concentrations were significantly higher in the repetition-based protocol compared to both the self-selected and fixed protocols (F = 7.3, p = 0.002, omega p2 = 0.252). No differences among protocols were found in fastest set velocity or perceived exertion (F = 0.3-2.8, p = 0.077-0.782). Regarding time efficiency, the fixed protocol was most efficient, followed by the repetition-based, with the self-selected protocol being least efficient. These findings indicate that the repetition-based approach offers an effective complementary strategy to traditional fixed and self-selected rest methods, especially in contexts emphasizing mechanical performance and session efficiency.

ABSTRACT:Nicholson, VP, Cowley, N, and Weakley, J. The effects of augmented feedback and encouragement during high-velocity resistance training on performance, fatigue, and function in older adults. J Strength Cond Res 40(6): e639-e646, 2026-This study aimed to assess whether kinematic feedback and verbal encouragement enhance movement velocity, motivation, perceived exertion, and physical performance in older adults within session. Fourteen older adults (9 females) with resistance training experience participated in a randomized cross-over trial. After baseline testing, subjects attended 4 resistance training sessions where they completed 3 sets of 10 repetitions on the leg press and bench press at 60% of their 3-repetition maximum. During each training session, subjects were exposed to one of 4 conditions-control (no feedback), visual kinematic feedback, verbal kinematic feedback, or verbal encouragement. The influence of training condition on mean concentric velocity (MCV), motivation, perceived exertion, functional performance, post 24-hour muscle soreness, and fatigue were assessed using a linear mixed model. For leg press, session MCV was significantly ( p < 0.05) higher for all experimental conditions compared with the control condition with effect sizes ranging from 0.16 (95% confidence interval [CI] 0.04-0.27) for verbal encouragement to 0.21 (95% CI 0.09-0.33) for visual and verbal kinematic feedback. For bench press, session MCV was significantly ( p < 0.05) higher for verbal kinematic feedback (ES = 0.20, 95% CI 0.10-0.30) and verbal encouragement (ES = 0.27, 95% CI 0.17-0.37) conditions compared with the control condition. Motivation, perceived exertion, functional performance, muscle soreness, and fatigue were not influenced by the improved movement velocity. These findings demonstrate that kinematic feedback and verbal encouragement can enhance resistance training quality in older adults, without impacting subsequent muscle soreness and function or perceptions of fatigue.

This study investigated the effects of resistance training (RT) proximity-to-failure on acute and short-term neuromuscular, perceptual, and metabolic responses, across different loads and exercises. In a randomised crossover design, 18 participants completed six RT sessions, performing six sets of the free-weight barbell back squat and prone row using a moderate (i.e. 65% one repetition maximum (1RM)) or heavy (i.e. 85% 1RM) load and different proximities-to-failure (i.e. momentary muscular failure, 1-repetition in reserve (RIR), or 3-RIR) for each condition. Participants returned 24 h later, assessing neuromuscular function and perceived muscle soreness. Increases in neuromuscular fatigue, perceptual fatigue, and metabolic stress were seen as RT was performed closer to momentary muscular failure, with back squats and 65% 1RM eliciting more fatigue than prone rows and 85% 1RM, respectively. Furthermore, within-session kinematics decreased as proximity-to-failure neared, except when performing prone rows with 85% 1RM. Additionally, more total repetitions were completed when training closer to momentary muscular failure with 65% 1RM back squats and both loads in the prone row. These findings demonstrate that while neuromuscular fatigue, perceptual fatigue, and metabolic stress increases with a closer proximity-to-failure, practitioners must also consider the exercise and load being used to maintain performance and mitigate fatigue.

The menstrual cycle can influence a range of physiological and psychological processes that may affect physical performance. However, existing evidence is inconsistent and often based on isolated testing timepoints rather than typical training conditions. Monitoring kinematic outputs during resistance training allows quantification of day-to-day performance changes. This study evaluated whether kinematic outputs during resistance training vary across menstrual cycle phases over two mesocycles and explored associations with symptoms, and perceived motivation and readiness. This study was conducted at Australian Catholic University (Brisbane, Australia) between February 2023 and June 2025 and was registered with the trial number ACTRN12626000365369. Twenty-eight resistance trained females (mean ± SD; age: 27.1 ± 5.2 years) completed two mesocycles of supervised resistance training. Across the intervention, menstrual cycles were monitored using calendar-based counting, urinary ovulation tests, and retrospective serum 17β-estradiol and progesterone concentrations. Three-repetition maximum (3RM) and load–velocity profiles (LVPs) for the bench press and trap bar deadlift were assessed at baseline, mid training ( 4 weeks), and post training ( 8 weeks). During each training session, kinematic outputs were recorded for all repetitions, with the fastest repetition from each set used to assess training performance across menstrual cycle phases and the observed velocity compared to the expected velocity from the LVP. Symptoms, perceived motivation, and readiness were reported at the start of each resistance training session. Significant differences in observed versus expected average peak mean velocity were found in the bench press during phases 1 and 5, and in the deadlift during phases 1 and 6. For both exercises, observed versus expected average peak mean velocity differed by 0.01–0.02 m·s⁻1 across menstrual cycle phases. After multivariate modeling, motivation to train was a strong predictor of training performance (β = 0.0004, p = 0.021), whereas readiness to perform was not. The symptom domain pain was positively associated with bench press performance (0.00065 m∙s−1 per unit change; p = 0.018), whereas pain was negatively associated with deadlift performance (− 0.001 per unit change; p < 0.001). Additionally, no significant main effect was found for the symptom domain control, but a between-exercise difference was found (p < 0.03). No other symptom domains showed significant relationships with training performance. Menstrual cycle phase appears to have minimal effect on resistance training performance, with kinematic outputs demonstrating modest differences compared to what would be expected. Consequently, these findings support consistent resistance training across the menstrual cycle, without the need for phase-based adjustments to maintain performance. However, motivation and symptom profiles may influence resistance training across the menstrual cycle.

PURPOSE:The aim of this study was to examine how practitioners currently quantify resistance training (RT), evaluate the perceived effectiveness of popular quantification methods, and identify barriers to quantifying RT load. METHODS:One hundred and fourteen practitioners (n = 114) who prescribe RT completed an international cross-sectional online survey between November 2023 and April 2024. The survey contained 41 questions, including open-ended, multiple-choice, and Likert-scale items. Descriptive statistics and chi-square tests were used to analyze quantitative data, and thematic analysis was used to analyze qualitative responses. RESULTS:Absolute volume load (82.5%) and session rating of perceived exertion load (77.2%) were the most common, whereas more complex methods like total work (12.3%) and system mass volume load (7.9%) were less commonly used. The most important variables identified by the practitioners were training frequency (75%), working sets (72%), and load (72%). Perceived efficacy of quantification methods was similar across experience groups; however, practitioners' perceptions of maximum dynamic strength volume load was significantly different, with a small to moderate effect. Practitioners with more than 10 years of experience rated relative volume load (75%) and session rating of perceived exertion load (73%) the highest, whereas those with less experience preferred absolute volume load (76%) and session rating of perceived exertion load (76%). The main barriers to RT quantification were measurement/methodological problems (50.5%), athlete-related difficulties (26.3%), and logistical/practical limitations (23.2%). Time constraints (46.7%) were the most common reason against monitoring RT, whereas tracking adaptation/progression (41.1%) and informing periodization/planning (24.7%) were the main reasons for doing so. CONCLUSIONS:The methods used to quantify RT load varied widely, with a clear preference for practical approaches. These findings highlight the need for improved education and standardized, practitioner-friendly methods to bridge the research-practice gap.

This exploratory study examined the effect of carbohydrate and subsequent energy availability (EA) on training adaptations, health, and sleep metrics across a 7-week preseason in semiprofessional female rugby union athletes. Full food provision on training days (four/week) manipulated carbohydrate intake: 6 versus 3 g·kg-1·day-1 for intervention (n = 7) and CON (n = 6), respectively while standardizing protein intake (>1.2 g·kg-1·day-1). Nontraining days involved ad libitum intake. Quantification of exercise energy expenditure via an individually calibrated wearable device allowed estimation of EA. Strength (three-repetition max squat, bench pull, and bench press), Bronco shuttle time; countermovement jump; biomarkers including iron studies, thyroid function, and cholesterol; sleep metrics; and physique (four-compartment model) were assessed before and after preseason. Training day carbohydrate intake was higher in intervention than CON (6.3 ± 0.2 vs. 3.1 ± 0.2 g·kg-1·day-1, p < .001), resulting in higher EA across the preseason (45.0 ± 5.8 vs. 29.9 ± 5.8 kcal·kg-1 fat-free mass-1·day-1, p < .001). Significant time effects were observed for bench pull (+3.6 kg, p = .024) and bench press (+4.6 kg, p = .011), fat mass (-1.1 kg, p < .001), fat-free mass (+1.2 kg, p = .025), and ferritin (-29 μg/L, p = .009). No significant interaction was observed for training adaptations, physique, or biochemical markers. Greater sleep duration was observed in CON on training nights, F(1, 156.072) = 5.821, p = .017, although other sleep metrics showed limited between-group differences. While increased carbohydrate intake improved EA, it had limited effect on training adaptations, health, or sleep metrics in this small, exploratory cohort.

Load-velocity profile (LVP) is an autoregulatory method in resistance training, yet the optimal LVP model for the free-weight jump squat (JS) remains unclear. This study therefore compared the accuracy of different LVP models and characterized their associated neuromuscular responses during the free-weight JS. In a cross-sectional, repeated-measures design, fifteen resistance-trained men first established their one-repetition maximum (1RM), and then performed free-weight JS at seven relative loads (20-80% 1RM). We compared generalized versus individualized, and linear versus polynomial LVP models. In addition, we measured mechanical outputs including peak and mean velocity, peak and mean power, and concentric phase time. Peak and mean neuromuscular activation of the vastus lateralis (VL) and medialis (VM) was measured using surface electromyography during the concentric phase. Individualized linear LVPs (R 2 = 0.98-0.99) demonstrated superior accuracy and less prediction error compared to generalized models (R 2 = 0.76-0.90), while polynomial models offered no advantage over linear models (p = 0.49-0.50). Peak and mean power was highest at 20% and 30% 1RM, respectively. Peak and mean muscle activation of the VL both peaked at 30% 1RM and decreased with increasing load. Practitioners should use individualized LVPs for accurate load prescription in the free-weight JS. Also, loads around 20-30% 1RM can induce high levels of acute mechanical power and VL muscle activation.

There is some evidence to indicate that lower-body compression garments aid recovery from exercise by improving sleep quality, but this evidence is based on measures derived from self-reports and accelerometers. The aim of this study was to examine the impact of wearing lower-body compression tights to bed on sleep following a bout of exercise, using the gold standard for sleep measurement. Twelve healthy males participated in a within-subjects, counterbalanced, randomized study with two conditions: (i) Treatment—wearing compression tights to bed after exercise, and (ii) Control—not wearing compression tights to bed after exercise. In both conditions, participants completed 40 min of moderate-intensity exercise in the afternoon and had a 9 h sleep opportunity at night. Objective and subjective assessments of sleep were obtained using polysomnography and visual analogue scales, respectively. Wearing compression tights to bed did not affect the objective measures, including sleep onset latency (p = 0.572); sleep efficiency (p = 0.754); total sleep time (p = 0.953); amount of slow-wave sleep (p = 0.374); and amount of rapid eye movement sleep (p = 0.638). Furthermore, wearing compression tights to bed did not affect the subjective measures, including sleep quality (p = 0.549), comfort (p = 0.548), and pain (p = 0.838). Wearing lower-body compression tights to bed after moderate-intensity exercise does not improve the quantity or quality of sleep obtained. Athletes who choose to wear compression tights to bed for the perceived benefits for recovery after exercise can do so without any undue effects on sleep.

BACKGROUND:This study aimed to develop an objective, repetition-based method for inter-set rest adjustment in resistance training (RT), addressing limitations of fixed and self-selected intervals. HYPOTHESIS:The adjustable protocol would yield greater repetition volume, higher velocity, and lower fatigue than the fixed protocol, with reduced time cost compared with the self-selected protocol. STUDY DESIGN:Crossover randomized trial. LEVEL OF EVIDENCE:Level 2. METHODS:A total of 20 young adults completed 3 RT protocols in randomized order: fixed, self-selected, and adjustable. Each protocol involved 5 sets of bench press and bench pull at approximately 75% 1 repetition maximal, performed close to failure. The protocols differed in inter-set rest strategy: fixed (3-minute rest), self-selected (participant-determined), and adjustable (based on previous set repetition count). RESULTS:Our findings indicated that the self-selected and adjustable protocols resulted in significantly greater repetition volume compared with the fixed protocol (F = 7.0; P = 0.003). Although the self-selected and adjustable protocols exhibited significantly higher fastest set velocity than the fixed protocol (F = 3.5, P = 0.04), the practical difference was only 0.01 m/s. In addition, no significant main effects of protocol were observed in mean set velocity, blood lactate concentration, or perceived exertion (F = 1.1-2.4; P = 0.10-0.58). In terms of time efficiency, the fixed protocol was the most efficient, followed by the adjustable protocol, with the self-selected protocol being the least efficient. CONCLUSION:People using the adjustable protocol achieved greater repetition volume without increased metabolic stress or perceived exertion compared with the fixed 3-minute rest. Although the adjustable protocol sacrifices some time efficiency compared with the fixed protocol, it remains more efficient than the self-selected approach. CLINICAL RELEVANCE:The repetition-based inter-set rest adjustment approach can serve as a feasible alternative to both fixed and self-selected inter-set rest approaches in RT.

This study aimed to: (1) quantify the accuracy of commercially available computer-vision and artificial intelligence (AI) player tracking software to measure player position, speed and distance covered using broadcast footage and (2) determine the impact of camera feed and resolution on accuracy. Data were obtained from one match at the 2022 Qatar Fédération Internationale de Football Association (FIFA) World Cup. Tactical, programme and camera 1 feeds were used. Three commercial tracking providers that use computer-vision and AI participated. Providers analysed instantaneous position (x, y co-ordinates) and speed (m·s −1 ) of each player. Their data were compared with a high-definition multi-camera tracking system (TRACAB Gen 5). Root mean square error (RMSE) and mean bias were calculated. Position RMSE ranged from 1.68 to 16.39 m, while speed RMSE ranged from 0.34 to 2.38 m·s −1 . Total distance mean bias ranged from −1745 m (−21.8%) to 1945 m (24.3%) across providers. Computer-vision and AI player tracking software offer the best accuracy when players are detected by the software. Providers should use a tactical feed when tracking position and speed, which will maximise player detection, improving accuracy. Both 720p and 1080p resolutions are suitable, assuming appropriate computer-vision and AI models are implemented.

Velocity zones (e.g., 1.0-0.75 m·s-1) are commonly aligned with terminology such as "starting strength", 'speed-strength', 'strength-speed', 'accelerative strength', or 'absolute strength'. However, the load-velocity profiles of most exercises do not align with these discrete bands. The aims of this study were to 1) develop load-velocity profiles of seven weightlifting derivatives; and 2) create exercise-specific velocity zones that can be used to guide training prescription. Fourteen (6 males and 8 females) weightlifting athletes undertook six testing sessions that required maximal strength testing on occasions one and two, and the development of load-velocity profiles for the power snatch, hang power clean, snatch pull, hang clean pull, hang power snatch, clean pull, and hang snatch pull on testing occasions three to six. During each testing occasion, peak velocity was assessed. Linear mixed models with effect size ±95% confidence limits (CL) were used to detect changes across profiles and estimate exercise specific velocity zones. While all load-velocity profiles had a clear reduction in velocity as load was increased, each exercise was found to have substantially different velocity zones when compared to previous recommendations. Of note, all 'absolute strength' zones (i.e., > 80% one repetition maximum) from the weightlifting derivatives were found to be greater than 1.3 m·s-1 which is commonly used as the threshold for 'starting strength'. These findings demonstrate that, if these terms are to be used, exercise-specific load-velocity profiles should be developed. Furthermore, these findings provide practitioners with exercise-specific zones that can be used to enhance training prescription and target specific strength qualities.

Accentuated eccentric loading (AEL) is a resistance training (RT) method applying greater eccentric- than concentric-phase load to intensify the training stimulus; however, despite its common use, a comprehensive and quantitative review remains lacking. The aim was to compare acute responses and chronic adaptations between AEL and constant-load RT, and examine whether effects vary by AEL protocol (submaximal, maximal, supramaximal). PubMed, Web of Science, Embase, and EBSCO were searched from inception through July 3, 2024; eligible English-language studies were included. Pooled and subgroup meta-analyses were performed using random-effects models. Forty-nine studies involving 773 participants were included. Although considerable variance exists in certain outcomes, our estimated effects suggest that, compared to constant-load RT, AEL results in (1) similar acute responses in loads lifted during the concentric phase (standardized mean difference [SMD] = − 0.16; p = 0.48), mechanical performance at submaximal loads during the concentric phase (SMD = − 0.07; p = 0.37), countermovement jump height both immediately (SMD = − 0.06; p = 0.86) and delayed (SMD = − 0.23; p = 0.44) post-intervention, maximal voluntary isometric force immediately post-intervention (SMD = 0.03; p = 0.89), blood lactate concentration during the intervention (SMD = − 0.06; p = 0.78), testosterone concentration immediately post-intervention (SMD = 0.68; p = 0.15), creatine kinase concentration both immediately (SMD = 0.09; p = 0.72) and delayed (SMD = 0.14; p = 0.48) post-intervention, cortisol concentration immediately post-intervention (SMD = 0.39; p = 0.05), heart rate during the intervention (SMD = 1.18; p = 0.07), acute muscle swelling immediately post-intervention (SMD = 0.26; p = 0.42), muscle electrical activity during the concentric phase (SMD = − 0.01; p = 0.90), and muscle soreness both immediately (SMD = 0.28; p = 0.30) and delayed (SMD = 0.18; p = 0.28) post-intervention; (2) greater acute responses in blood lactate concentration immediately post-intervention (SMD = 0.44; p = 0.03), growth hormone concentration immediately post-intervention (SMD = 0.50; p = 0.01), muscle electrical activity during the eccentric phase (SMD = 0.37; p = 0.01), and rating of perceived exertion immediately post-intervention (SMD = 1.72; p = 0.01); (3) similar chronic adaptations in maximal concentric strength (SMD = 0.12; p = 0.41), maximal eccentric strength (SMD = 0.19; p = 0.58), maximal isometric strength (SMD = 0.03; p = 0.93), countermovement jump height (SMD = 0.04; p = 0.87), muscle fascicle angle (SMD = − 0.10; p = 0.77), muscle fascicle length (SMD = 0.90; p = 0.17), and muscle cross-sectional area (SMD = − 0.06; p = 0.84). While AEL augments the eccentric-phase stimulus (higher eccentric load and muscle electrical activity), it also increases metabolic stress and perceived effort, implying a need for longer, more frequent inter-set rests and longer between-session recovery. Given the lack of evidence for superior chronic benefits in strength or muscle architecture over constant-load RT, practitioners should consider these factors carefully. The original protocol for this review was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO) in July 2024 (CRD42024561673).

INTRODUCTION & AIMS: Concerns about the replicability of sport science findings have intensified. Particularly, performance-based interventions are susceptible to inflated effects due to methodological variability and selective reporting. Post-activation performance enhancement (PAPE) is widely applied to acutely improve ballistic performance, yet reported effects are inconsistent. This pre-registered study aimed to conceptually replicate a previously used PAPE protocol and investigate its acute effects on countermovement jump (CMJ) height and CMJ height variance. METHODS: Forty-nine trained athletes (31 men, 18 women) completed a multi-centre, randomised control trial across four separate sites. Following a familiarisation session, participants completed a standardised warm-up and two PAPE sessions involving a single set of barbell back-squats (3 × 90% 1RM) and two control sessions with time-matched active rest. Session order was counterbalanced, randomised, and separated by one-week periods. CMJ height was assessed with force platforms at baseline and immediately (time 0), 3, 6, 9, and 12 minutes after the set of barbell back-squats. A generalised additive mixed model for location, shape, and scale modelled the location (i.e. mean) and scale (i.e. standard deviation [SD]) of CMJ height across time. Baseline CMJ height was used as a covariate to account for regression-to-the-mean. RESULTS: Mean CMJ height for PAPE was significantly lower immediately after the intervention compared to control (32.75cm [95%CI: 32.29-33.14] vs. 33.86cm [95%CI: 33.65-34.14]). CMJ height SD for PAPE was significantly higher than control immediately after the intervention (2.00cm [95%CI: 1.60-2.37] vs. 1.38 [95%CI: 1.11-1.57]). Qualitatively, the time course of CMJ height is similar at minutes 3 through 12, and CMJ height SD is only similar by minute 12. CONCLUSION: The results indicate PAPE decreases CMJ jump height while nearly doubling CMJ height variance. Together with methodological variability and selective reporting (small samples, p-hacking, etc.), this increased variance is likely the cause of the widespread belief that PAPE exists.

Powerlifting is a strength sport featuring some of the world’s strongest athletes. Recent decades have seen an exponential increase in research into the applied sport science and medicine of powerlifting and its Paralympic counterpart, para powerlifting. A scoping review of the area would provide athletes, coaches, policymakers, and researchers with an overview of the existing evidence to support performance, reduce injury, and foster further growth of these sports. The primary objectives were to identify the current research into the applied sport science and medicine of powerlifting and para powerlifting, analyse the characteristics of the research, provide a brief summary of the research in each area of sport science and medicine, identify gaps in the current literature, and provide recommendations for future research. Systematic searches of SPORTDiscus, CINAHL, MEDLINE, and Scopus were performed from the earliest record to June 2025 (Open Science Framework registration: https://osf.io/fkjsz ), and the reference lists of several pre-existing systematic reviews were manually searched. Studies were eligible for inclusion if they investigated powerlifting or para powerlifting as a sport or the applied sport science of powerlifters or para powerlifters from a performance or injury perspective. A total of 2117 articles were identified in the database search, with three additional eligible studies discovered through other sources. In total, 218 studies met the inclusion criteria and were ultimately included in the review. The most researched sport science and medicine topic was physical qualities (n = 48), followed by competition (n = 45), training (n = 38), biomechanics (n = 36), nutrition and supplementation (n = 25), injury (n = 18), and psychology (n = 8). More than half of the included studies were published in 2020 or later, and researchers from the USA were the most prolific with 57 publications. Para powerlifting was investigated in 45 studies, which mostly originated from Brazil (n = 31). Participants represented varying levels of competition, powerlifting divisions, and age categories, although many studies did not clearly report these characteristics. Only seven studies investigated female athletes exclusively. This scoping review summarises the current literature investigating powerlifting and para powerlifting and can be used to enhance the applied sport science and medicine within the sports. While the amount of research has grown considerably in recent years, it is evident that certain demographics and areas remain under-investigated (e.g., injury mechanisms) or warrant updated examination (e.g., the prevalence of performance-enhancing drug use, which was last reported in 2003 and is currently unknown). Thus, this review highlights several areas for future research based on the gaps in the existing literature and provides a range of recommendations that can be implemented to improve reporting, transparency, and interpretation.

Both maximal strength and speed-strength are considered key aspects of rugby league and rugby union match-play yet represent distinct physical qualities. Establishing whether maximal strength or speed-strength has a greater association with game performance in these sports can help direct resistance training interventions towards the physical quality most likely to transfer to the outcome of interest. It is therefore important to develop a clearer understanding of whether it is maximal strength or speed-strength that has stronger links to key aspects of rugby union and rugby league competition. To systematically review and meta-analyse the literature to compare maximal strength versus speed-strength measures in their associations with within-game key performance indicators (KPIs) in rugby union and rugby league. Further, sub-group analysis was undertaken to determine whether the type of KPI (tackles, rucks, line breaks, carries/hit-ups, errors, and miscellaneous) moderated the extent to which the observed effect favoured maximal strength or speed-strength for a given KPI. A systematic search, conducted in accordance with PRISMA guidelines, of Web of Science, PubMed, and SportDiscus was conducted up to December 2024. Studies meeting the inclusion criteria provided correlation coefficients that were converted into Fisher’s z-transformed effect sizes to standardise the measures and allow for consistent comparison across studies. These effect sizes were analysed using a multivariate meta-analysis framework. Six studies containing 41 maximal strength versus speed-strength comparisons and 134 players were included in the final analysis. Of the 786 studies initially identified, 28 underwent full-text screening, with 22 ultimately excluded. The overall pooled effect was 0.24 [0.11: 0.37] in favour of strength; however, the prediction interval ranged from g = − 0.58 to g = 1.06. The moderator analysis revealed a statistically significant effect of type (p = 0.038), indicating stronger associations for maximal strength compared to speed-strength. Residual heterogeneity (p = 0.005) suggested substantial variability in effect sizes across studies that was not fully explained by the model. Within-game rugby union and rugby league KPIs typically have stronger links to maximal strength than to speed-strength. However, the wide prediction interval underscores that the observed maximal strength advantage may not generalise across all types of KPIs or predictably translate to all future settings.