Supervision during resistance training (RT) may enhance strength gains by optimizing trainee effort. We investigated supervision’s role on effort during RT in a unique setting with private strength clinics, where members train either unsupervised (“Core” membership) or supervised by a qualified exercise scientist (“Assisted” membership). Using both retrospective analysis of member training records and a prospective experimental study, we examined supervision’s impact on exercise performance, measured as time under load (TUL) where a longer TUL at a given load is indicative of greater effort, rating of perceived effort (RPE), and rating of perceived discomfort (RPD). Bayesian methods were applied, using empirically informed prior distributions from retrospective data to model the experimental study. The previous observational sample included ~1000 members training sessions from each membership type, while the experimental study involved 45 Core members performing both supervised and unsupervised sessions in randomized order, using their current training loads to momentary failure. Our findings suggest that, in real-world settings (in situ), exercise performance differed little between supervised and unsupervised training with trainees likely using suboptimal effort under both conditions. However, in our experimental study, supervision improved TUL (Core = 125.12 [95%QI: 113.70, 131.90] sec; Assisted = 147.35 [95%QI: 134.29, 154.81] sec; contrast = -22.10 [95%QI: -26.60, -17.61] sec). In percentage points RPE was slightly higher with supervision in both previous observational real-world (Core = 53% [95%QI: 51%, 55%]; Assisted = 59% [95%QI: 57%, 61%]; contrast = -6% [95%QI: -8%, -4%]) and experimental settings (Core = 81% [95%QI: 75%, 86%]; Assisted = 87% [95%QI: 83%, 91%]; contrast = -6% [95%QI: -10%, -4%]), suggesting trainees push closer to failure under supervision. This was further supported by higher RPD during the experimental study (Core = 6.3 [95%QI: 5.1, 7.3]; Assisted = 7.5 [95%QI: 6.5, 8.3]; contrast = -1.2 [95%QI: -1.6, -0.9]). Overall, these results reinforce research on the benefits of supervision in RT, indicating that, during a single session under experimental conditions, trainees likely train with greater effort when supervised.

This project represents a highly powered pre-registered comparison of full ROM (fROM) and 'lengthened partial' ROM (lpROM) resistance training [RT]. A randomized controlled cluster trial across 15 sites was employed. The outcomes were muscle cross sectional area (CSA) of the upper arm and thigh, and strength for chest press, leg press, and pulldown. Pre-testing preceded randomization to either the lpROM (n = 163) or fROM (n = 134) RT condition. Post-testing was completed following a 12-week intervention. Our primary estimand of interest was the condition by time interaction. The estimate for this effect for arm estimated muscle CSA was -0.032 and for thigh estimated muscle CSA was 0. The p-values for equivalence were p = 0.071 for the arm muscle, and p = 0.019 for the thigh muscle. Inference criteria with alpha were set at 0.01 and adjusted to 0.005 for multiple outcomes, as such, we were unable to reject the null hypothesis that the condition:time interaction effect was outside of the SESOI [-0.1, 0.1]. Exploratory analysis suggests that both the main effects of time, and any interaction effects for condition by time, are likely small. These findings support previous evidence comparing fROM and lpROM specifically and suggest that between condition effects are small and practically equivalent.

Previous research indicates the importance of the countermovement jump (CMJ) test to monitor lower-limb power and neuromuscular fatigue. While jump height (JH) can be measured using various equipment, this study compared the JH obtained from the Just Jump System (JJS), OptoJump and the MyJump2 app against the Vald ForceDecks system using the impulse-momentum calculation method, which is regarded as the gold standard method to calculate JH. This study also assessed the one-week test-retest reliability of these pieces of equipment. The participants in this study were 20 (n = 12 male and n = 8 female) university sports students and staff (mean ± SD; age: 20.90 ± 2.63 years; stature: 1.76 ± 0.10 m; mass: 72.17 ± 11.07 kg). Participants completed a standardised warm-up and rested for three minutes before completing three CMJs on each piece of equipment in a randomised, counterbalanced order. The same protocols were used in the second session, with a different equipment testing order. Both MyJump and OptoJump have high agreement levels (Mean bias and 95% CI = 2.32 cm [1.57 – 3.09] and 1.92 cm [1.23 – 2.59], respectively) with the gold measurement standard (ForceDecks using IM). However, a high mean bias for the JJS (Mean bias = 9.88 cm [9.26 – 10.46]) was reported. This study also found that all methods are reliable for assessing JH (Mean bias and [95% CI]: ForceDecks = 0.24 cm [-0.47 – 0.92], JJS = 0.74 cm [0.08 -1.42], MyJump = 0.05 cm [-0.57 – 0.71] and Optojump = -0.14 cm [-0.77-0.49]). Overall, the equipment investigated in this study showed high levels of reliability, and only the JJS had low validity compared to the ForceDecks. Coaches should consider what data they want to collect, its validity and reliability, the purpose of the testing and the cost of the equipment when deciding which system to purchase.

In the present paper we demonstrate the application of methods for cumulative evidence synthesis including Bayesian meta-analysis, and exploration of questionable research practices such as publication bias or p-hacking, in the sport and exercise sciences for the evaluation of experimental interventions. The use of such methods can aid in study planning and avoid “research waste”. In demonstrating and discussing these methods we use the example of self-talk interventions and their effects upon sport/motor performance given a quantitative evidence synthesis has not been conducted on this topic, to the best of our knowledge, since 2011 when Hatzigeorgiadis et al. (2011) conducted their systematic review and meta-analysis. As such, this topic is ripe to use in demonstrating cumulative methods such as Bayesian updating. Therefore, our aim was to conduct an updated systematic review and Bayesian meta-analysis replicating the search, inclusion, and models of Hatzigeorgiadis et al. (2011) and demonstrate the application of cumulative evidence synthesis methods including; consideration of the initial probability that a new study of the effects of self-talk interventions would shift our prior belief in their effectiveness, the application of priors taken from the previous meta-analysis to be updated by new studies identified to a new posterior estimate of effect, and consideration of other possible sources of research waste from questionable research practices such as publication bias and p-hacking. Such methods as those demonstrated here, when used prospectively, can aid researchers in determining whether further research of a particular experimental intervention is in fact warranted. Considering the limited resources and time for conducting research we hope that highlighting the application of these methods might help researchers in the field to avoid research waste and more productively direct their research efforts.

Wheelchair rugby (WCR) is an indoor contact sport. The sport is commonly known for its paralympic discipline, WCR Fours. A more inclusive version of the sport, WCR Fives, was developed recently. Previously, it has been reported that sprint and repeated sprint (RS) ability are crucial for success in WCR. However, very little is known about the differences in these qualities between those playing WCR Fours and Fives, or between those with a spinal cord injury (SCI) and those without, in recreational WCR players. Therefore, this study aimed to address these gaps in a non-elite sample of athletes. A total of 21 (17 males and four females; SCI n = 10, Non-SCI n = 11) players (mean ± SD; age: 34.66 ± 12.34 years; mass: 76.23 ± 21.96 kg; stature: 1.76 ± 0.09 m) participated. This study measured velocity (m·s-1) and acceleration (m·s-2) with splits at 5, 10, 15, and 20m during three maximal 20m sprint efforts and timing splits during 10 x 20m RSs. Fours and Fives showed similar velocities and accelerations across all distances during the initial sprints. SCI participants had slower velocities and lower acceleration across all distances. However, there were interactions between disability and distance where although SCI participants had lower accelerations over the initial 0-5m distance, the difference decreased as the distance covered increased. During the RSs, similar performances across all distances and all sprint numbers were observed for Fours and Fives and SCI and non-SCI players. In conclusion, there appears to be little difference between Fours and Fives sprint and RS ability.

The aim of this review was to examine how mean muscle length during resistance training (RT) influences regional muscle hypertrophy. Three databases were screened for relevant studies that manipulated muscle length through range of motion or exercise selection and evaluated regional muscle hypertrophy. Twelve studies conducted among young adults were included in the Bayesian meta-analysis. Standardized mean differences (SMDs) indicated trivial hypertrophic effects estimated with relatively high precision between proximal (25% muscle length; SMD: 0.05 [95% quantile interval {QI}:-0.07, 0.16]; exponentiated log-transformed response ratio [lnRR]: 0.57% [95% QI:-1.92%, 3.24%]), mid-belly (50% muscle length; SMD: 0.07 [95% QI:-0.02, 0.15]; exponentiated lnRR: 1.22% [95% QI:-0.77%, 3.22%]), and distal (75% muscle length; SMD: 0.09 [95% QI:-0.01, 0.19]; exponentiated lnRR: 1.88% [95% QI:-0.44%, 4.34%]) sites. The effects of RT at longer muscle lengths showed an increasing trend from proximal to distal sites. However, the percentage of posterior distributions falling within regions of practical equivalence was high across all sites. Our findings suggest that RT at both longer and shorter mean muscle lengths produces similar hypertrophic effects. Relatively small differences between "shorter" and "longer" mean muscle length (an average difference of 21.8% mean muscle length) between conditions/groups in the examined studies warrant caution when interpreting the findings.

Jump height (JH) achieved in a countermovement jump (CMJ) has been suggested to allow for the monitoring of neuromuscular fatigue (NMF) and assessment of lower body power. Although force platforms (FP) are considered the gold standard for measuring CMJ height, they are expensive compared to mobile apps such as My Jump Lab (MJL). Therefore, this study aimed to assess the concurrent validity and agreement of the MJL app compared to a FP (ForceDecks [FD]) system and to determine its test-rest reliability. A convenience sample of 26 (n = 11 females and n = 15 males) recreationally active university sport students and staff (mean ± SD; age: 23.08 ± 6.33 years; mass: 72.85 ± 9.93 kg; stature: 176.63 ± 10.18 cm) participated in the study. Participants attended the laboratory for testing on two separate occasions, separated by one week. After a standardised warm-up, they completed three CMJs on each occasion, with CMJ height simultaneously assessed by the FD and MJL app. The MJL Artificial Intelligence mode showed a mean bias of 4.32 cm [95% CI: 3.4, 5.26] overestimation with 95% limits of agreement ranging from -3.33 cm [95% CI: -4.96, -0.85] to 11.98 cm [95% CI: 10.13, 13.41]. Both methods demonstrated minimal mean bias (FD = 0.61 cm [95% CI: -0.31, 1.37] and MJL = 0.25 cm [95% CI = -0.48, 0.98]) between sessions, and both showed a similar width to their limits of agreement, ranging ~7 cm about the mean bias. In summary, the MLJ overestimated CMJ height in this sample compared to the FD system, but both methods were reliable. Given the significant differences in cost for these two methods, teams on a budget may interested in trialling the MJL app.

Purpose There is growing emphasis on investigating heterogeneity in resistance training (RT) outcomes, likely motivated by observations of substantial gross variability in training effects. However, gross variability does not necessarily represent true inter-individual response variation (IRV) and can be obscured by measurement error, sampling variance, and biological variability. Appropriate study design and statistical analysis are required to distinguish IRV from these confounding sources of within-participant variation. Methods 16 recreationally trained participants completed a novel replicated within-participant unilateral design across two 11-week training phases separated by a 6-8 week washout. Lower limbs were randomized to a low volume (∼8 sets/week) or high volume (∼16 sets/week) training protocol in each phase. We assessed both general (GEN; average response across conditions) and condition-specific (CON; difference between volumes) IRV for vastus lateralis cross-sectional area and leg press one-repetition maximum using a multi-stage statistical approach. Results Higher weekly set volumes demonstrated a detectable advantage for muscle hypertrophy (1.8 cm² [95% HDI: 0.29, 3.41]; 98.77% posterior probability) but not maximal strength (3.48 kg [95% HDI: -5.1, 12.15]; 80.01% posterior probability). Despite substantial gross variability, we failed to detect irrefutable evidence of meaningful IRV. Integrated methods revealed stronger evidence for GEN versus CON IRV, with correlation coefficients ranging from 0.67 to 0.7 for GEN versus 0.04 to 0.06 for CON. Conclusions Our findings clearly illustrate that gross variability in training outcomes does not necessarily indicate true inter-individual differences, a distinction critical for both research and practice. KEY POINTS ### Competing Interest Statement Zac P. Robinson, Joshua C. Pelland, Jacob F. Remmert, Michael C. Zourdos, Eric R. Helms, and Eric T. Trexler are all coaches and writers in the fitness industry. James Steele provides research consultancy. All other authors declare that they have no conflicts of interest relevant to the content of this study. * RT : Resistance training IRV : Inter-individual response variation WPV : Within-participant variation GEN : General inter-individual response variation CON : Condition-specific inter-individual response variation ATE : Average training effect ITE : Individual training effects SDIRV : Standard deviation of inter-individual response variation SDWPV : Standard deviation of within-participant variation LV : Low volume condition (i.e., 6-8 sets per week) HV : High volume condition (i.e., 12-16 sets per week) VL : Vastus lateralis CSA : Cross-sectional area 1RM : One-repetition maximum MT : Muscle thickness MVIC : Maximal voluntary isometric contraction RIR : Repetitions in reserve MPV : Mean propulsive velocity sRPE : Session rating of perceived exertion ICC : Intraclass correlation coefficient SEM : Standard error of measurement CV : Coefficient of variation CI : Confidence interval PI : Prediction interval lnVR : Log variability ratio DiV : Difference in variability LOA : Limits of agreement ANCOVA : Analysis of covariance HDI : Highest density interval HDPI : Highest density prediction interval DICOM : Digital Imaging and Communications in Medicine Renaissance Periodization, Project ID: 003981

BACKGROUND:The replicability of sports and exercise research has not been assessed previously despite concerns about scientific practices within the field. AIM:This study aims to provide an initial estimate of the replicability of applied sports and exercise science research published in quartile 1 journals (SCImago journal ranking for 2019 in the Sports Science subject category; www.scimagojr.com ) between 2016 and 2021. METHODS:A formalised selection protocol for this replication project was previously published. Voluntary collaborators were recruited, and studies were allocated in a stratified and randomised manner on the basis of equipment and expertise. Original authors were contacted to provide deidentified raw data, to review preregistrations and to provide methodological clarifications. A multiple inferential strategy was employed to analyse the replication data. The same analysis (i.e. F test or t test) was used to determine whether the replication effect size was statistically significant and in the same direction as the original effect size. Z-tests were used to determine whether the original and replication effect size estimates were compatible or significantly different in magnitude. RESULTS:In total, 25 replication studies were included for analysis. Of the 25, 10 replications used paired t tests, 1 used an independent t test and 14 used an analysis of variance (ANOVA) for the statistical analyses. In all, 7 (28%) studies demonstrated robust replicability, meeting all three validation criteria: achieving statistical significance (p < 0.05) in the same direction as the original study and showing compatible effect size magnitudes as per the Z test (p > 0.05). CONCLUSION:There was a substantial decrease in the published effect size estimate magnitudes when replicated; therefore, sports and exercise science researchers should consider effect size uncertainty when conducting subsequent power analyses. Additionally, there were many barriers to conducting the replication studies, e.g., original author communication and poor data and reporting transparency.

OBJECTIVES:In evidence synthesis, inconsistency is typically assessed visually and with the I2 and the Q statistics. However, these measures have important limitations (i) if there are few primary studies of small sample sizes or (ii) if there are multiple studies with precise estimates. In addition, with the increasing use of decision thresholds (DT), for example in Grading of Recommendations Assessment, Development and Evaluation evidence to decision (EtD) frameworks, inconsistency judgments can be anchored around DTs. In this article, we developed quantitative measures to assess inconsistency based on DTs. STUDY DESIGN AND SETTING:We developed two measures to quantify inconsistency based on DTs - the decision inconsistency (DI) and the across-studies inconsistency (ASI) indices. The DI and the ASI are based on the distribution of the posterior samples studies' effect sizes (ES) across interpretation categories defined by DTs. We developed these indices for the Bayesian context, followed by a frequentist extension. RESULTS:The DI informs on the overall inconsistency of ESs across interpretation categories, while the ASI quantifies how different studies are compared to each other (in relation to interpretation categories) based on absolute effects. A DI ≥ 50% and an ASI ≥ 25% are suggestive of important inconsistency. We provide an R package (metainc) and a web tool (https://metainc.med.up.pt/) to support the computation of the DI and ASI, including in the context of sensitivity analyses assessing the impact of potential uncertainty in inconsistency. CONCLUSION:The DI and the ASI can contribute to quantitatively assess inconsistency, particularly as DTs are gaining recognition in evidence synthesis and health decision-making.

BACKGROUND:The proximity to failure in which sets are terminated has gained attention in the scientific literature as a potentially key resistance training variable. Multiple meta-analyses have directly (i.e., failure versus not to failure) or indirectly (e.g., velocity loss, alternative set structures) evaluated the effect of proximity to failure on strength and muscle hypertrophy outcomes categorically; however, the dose-response effects of proximity to failure have not been analyzed collectively in a continuous manner. OBJECTIVE:To meta-analyze the aforementioned areas of relevant research, proximity to failure was quantified as the number of repetitions in reserve (RIR). Importantly, the RIR associated with each effect in the analysis was estimated on the basis of the available descriptions of the training interventions in each study. Data were extracted and a series of exploratory multilevel meta-regressions were performed for outcomes related to both strength and muscle hypertrophy. A range of sensitivity analyses were also performed. All models were adjusted for the effects of load, method of volume equating, duration of intervention, and training status. RESULTS:The best fit models for both strength and muscle hypertrophy outcomes demonstrated modest quality of overall fit. In all of the best-fit models for strength, the confidence intervals of the marginal slopes for estimated RIR contained a null point estimate, indicating a negligible relationship with strength gains. However, in all of the best-fit models for muscle hypertrophy, the marginal slopes for estimated RIR were negative and their confidence intervals did not contain a null point estimate, indicating that changes in muscle size increased as sets were terminated closer to failure. CONCLUSIONS:The dose-response relationship between proximity to failure and strength gain appears to differ from the relationship with muscle hypertrophy, with only the latter being meaningfully influenced by RIR. Strength gains were similar across a wide range of RIR, while muscle hypertrophy improves as sets are terminated closer to failure. Considering the RIR estimation procedures used, however, the exact relationship between RIR and muscle hypertrophy and strength remains unclear. Researchers and practitioners should be aware that optimal proximity to failure may differ between strength and muscle hypertrophy outcomes, but caution is warranted when interpreting the present analysis due to its exploratory nature. Future studies deliberately designed to explore the continuous nature of the dose-response effects of proximity to failure in large samples should be considered.

Depends R (>= 4.0.0)Imports meta (>= 7.0-0), ggplot2, confintr Suggests metafor Description Assessment of inconsistency in meta-analysis by calculating the Decision Inconsistency index (DI) and the Across-Studies Inconsistency (ASI) index.These indices quantify inconsistency taking into account outcome-level decision thresholds.