The Effects of Sprint Interval Training on Physical Performance: A Systematic Review and Meta-Analysis.

ABSTRACT: Hall, AJ, Aspe, RR, Craig, TP, Kavaliauskas, M, Babraj, J, and Swinton, PA. The effects of sprint interval training on physical performance: a systematic review and meta-analysis. J Strength Cond Res 37(2): 457-481, 2023-The present study aimed to synthesize findings from published research and through meta-analysis quantify the effect of sprint interval training (SIT) and potential moderators on physical performance outcomes (categorized as aerobic, anaerobic, mixed aerobic-anaerobic, or muscular force) with healthy adults, in addition to assessing the methodological quality of included studies and the existence of small study effects. Fifty-five studies were included (50% moderate methodological quality, 42% low methodological quality), with 58% comprising an intervention duration of ≤4 weeks and an array of different training protocols. Bayesian's meta-analysis of standardized mean differences (SMD) identified a medium effect of improved physical performance with SIT (ES 0.5 = 0.52; 95% credible intervals [CrI]: 0.42-0.62). Moderator analyses identified overlap between outcome types with the largest effects estimated for anaerobic outcomes (ES 0.5 = 0.61; 95% CrI: 0.48-0.75). Moderator effects were identified for intervention duration, sprint length, and number of sprints performed per session, with larger effects obtained for greater values of each moderator. A substantive number of very large effect sizes (41 SMDs > 2) were identified with additional evidence of extensive small study effects. This meta-analysis demonstrates that short-term SIT interventions are effective for developing moderate improvements in physical performance outcomes. However, extensive small study effects, likely influenced by researchers analyzing many outcomes, suggest potential overestimation of reported effects. Future research should analyze fewer a priori selected outcomes and investigate models to progress SIT interventions for longer-term performance improvements.

Interpreting magnitude of change in strength and conditioning: Effect size selection, threshold values and Bayesian updating.

The magnitude of change following strength and conditioning (S&C) training can be evaluated comparing effect sizes to thresholds. This study conducted a series of meta-analyses and compiled results to identify thresholds specific to S&C, and create prior distributions for Bayesian updating. Pre- and post-training data from S&C interventions were translated into standardised mean difference (SMDpre) and percentage improvement (%Improve) effect sizes. Bayesian hierarchical meta-analysis models were conducted to compare effect sizes, develop prior distributions, and estimate 0.25-, 0.5-, and 0.75-quantiles to determine small, medium, and large thresholds, respectively. Data from 643 studies comprising 6574 effect sizes were included in the analyses. Large differences in distributions for both SMDpre and %Improve were identified across outcome domains (strength, power, jump and sprint performance), with analyses of the tails of the distributions indicating potential large overestimations of SMDpre values. Future evaluations of S&C training will be improved using Bayesian approaches featuring the information and priors developed in this study. To facilitate an uptake of Bayesian methods within S&C, an easily accessible tool employing intuitive Bayesian updating was created. It is recommended that the tool and specific thresholds be used instead of isolated effect size calculations and Cohen's generic values when evaluating S&C training.

High-Intensity Cycling Training: The Effect of Work-to-Rest Intervals on Running Performance Measures.

The work-to-rest ratio during cycling-based high-intensity interval training (HIT) could be important in regulating physiological and performance adaptations. We sought to determine the effectiveness of cycling-based HIT with different work-to-rest ratios for long-distance running. Thirty-two long-distance runners (age: 39 ± 8 years; sex: 14 men, 18 women; average weekly running training volume: 25 miles) underwent baseline testing (3-km time-trial, V[Combining Dot Above]O2peak and time to exhaustion, and Wingate test) before a 2-week matched-work cycling HIT of 6 × 10-second sprints with different rest periods (30 seconds [R30], 80 seconds [R80], 120 seconds [R120], or control). Three-kilometer time trial was significantly improved in the R30 group only (3.1 ± 4.0%, p = 0.04), whereas time to exhaustion was significantly increased in the 2 groups with a lower work-to-rest ratio (R30 group 6.4 ± 6.3%, p = 0.003 vs. R80 group 4.4 ± 2.7%, p = 0.03 vs. R120 group 1.9 ± 5.0%, p = 0.2). However, improvements in average power production were significantly greater with a higher work-to-rest ratio (R30 group 0.3 ± 4.1%, p = 0.8 vs. R80 group 4.6 ± 4.2%, p = 0.03 vs. R120 group 5.3 ± 5.9%, p = 0.02), whereas peak power significantly increased only in the R80 group (8.5 ± 8.2%, p = 0.04) but not in the R30 group (4.3 ± 6.1%, p = 0.3) or in the R120 group (7.1 ± 7.9%, p = 0.09). Therefore, cycling-based HIT is an effective way to improve running performance, and the type and magnitude of adaptation is dependent on the work-to-rest ratio.

Electromyographic and kinetic comparison of the back squat and overhead squat.

The purpose of this study was to compare muscle activity and kinetics during the back squat and overhead squat performed at 3 relative intensities (60, 75, and 90% 3 repetition maximum). Fourteen subjects (age, 26 ± 7 years; height, 182.5 ± 13.5 cm; body mass, 90.5 ± 17.5 kg) performed each exercise using a within-subjects crossover design. In addition, a selection of trunk isolation exercises were included to provide additional comparisons. Squats were performed on a force platform with electromyographic activity of the anterior deltoid, rectus abdominis (RA), external oblique (EO), erector spinae (ES), gluteus maximus, vastus lateralis, biceps femoris, and lateral gastrocnemius recorded throughout. The overhead squat demonstrated significantly greater (p ≤ 0.05) activity in the anterior trunk muscles (RA and EO) during the eccentric phase. However, the magnitudes of the differences were relatively small (approximately 2-7%). In contrast, the back squat displayed significantly greater (p ≤ 0.05) activity in the posterior aspect of the trunk ES and all lower-body muscles during the concentric phase. Kinetic comparisons revealed that significantly greater peak force (p ≤ 0.05) was developed during the back squat. Electromyographic comparisons between the trunk isolation exercises and squat variations demonstrated substantially greater anterior trunk activity during the isolation exercises, whereas the highest activity in the posterior aspect of the trunk was obtained during the squats (p ≤ 0.05). The results of the study do not support the hypothesis that the overhead squat provides a substantially greater stimulus for developing the trunk musculature compared with the back squat.

The Predictive Validity of Individualised Load-Velocity Relationships for Predicting 1RM: A Systematic Review and Individual Participant Data Meta-analysis.

BACKGROUND: Load-velocity relationships are commonly used to estimate one-repetition maximums (1RMs). Proponents suggest these estimates can be obtained at high frequencies and assist with manipulating loads according to session-by-session fluctuations. Given their increasing popularity and development of associated technologies, a range of load-velocity approaches have been investigated. OBJECTIVE: This systematic review and individual participant data (IPD) meta-analysis sought to quantify the predictive validity of individualised load-velocity relationships for the purposes of 1RM prediction. METHODS: In September 2022, a search of MEDLINE, SPORTDiscus, Web of Science and Scopus was conducted for published research, with Google Scholar, CORE and British Ethos also searched for unpublished research. Studies were eligible if they were written in English, and directly compared a measured and predicted 1RM using load-velocity relationships in the squat, bench press, deadlift, clean or snatch. IPD were obtained through requests to primary authors and through digitisation of in-text plots (e.g. Bland-Altman plots). Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the review conducted in accordance with PRISMA-IPD guidelines and an a priori protocol. Absolute and scaled standard error of the estimates (SEE/SEE%) were calculated for two-stage aggregate analyses, with bootstrapping performed for sampling variances. Estimates were pooled using three-level hierarchical models with robust 95% confidence intervals (CIs). One-stage analyses were conducted with random intercepts to account for systematic differences across studies and prediction residuals calculated in the absolute scale (kg) and as a percentage of the measured 1RM. Moderator analyses were conducted by including a priori defined categorical variables as fixed effects. RESULTS: One hundred and thirty-seven models from 26 studies were included with each identified as having low, unclear or high risk of bias. Twenty studies comprising 434 participants provided sufficient data for meta-analyses, with raw data obtained for 8 (32%) studies. Two-stage analyses identified moderate predictive validity [SEE% 9.8, 95% CI 7.4% to 12.2%, with moderator analyses demonstrating limited differences based on the number of loads (ß2Loads:>2Loads = 0.006, 95% CI - 1.6 to 1.6%) or the use of individual or group data to determine 1RM velocity thresholds (ßGroup:Individualised = - 0.4, 95% CI - 1.9 to 1.0%)]. One-stage analyses identified that predictions tended to be overestimations (4.5, 95% CI 1.5 to 7.4 kg), which expressed as a percentage of measured 1RM was equal to 3.7 (95% CI 0.5 to 6.9% 1RM). Moderator analyses were consistent with those conducted for two-stage analyses. CONCLUSIONS: Load-velocity relationships tend to overestimate 1RMs irrespective of the modelling approach selected. On the basis of the findings from this review, practitioners should incorporate direct assessment of 1RM wherever possible. However, load-velocity relationships may still prove useful for general monitoring purposes (e.g. assessing trends across a training cycle), by providing high-frequency estimates of 1RM when direct assessment may not be logistically feasible. Given limited differences in predictions across popular load-velocity approaches, it is recommended that practitioners opting to incorporate this practice select the modelling approach that best suits their practical requirements. REGISTRATION: https://osf.io/agpfm/ .

Autoregulation in Resistance Training: Addressing the Inconsistencies.

Autoregulation is a process that is used to manipulate training based primarily on the measurement of an individual's performance or their perceived capability to perform. Despite being established as a training framework since the 1940s, there has been limited systematic research investigating its broad utility. Instead, researchers have focused on disparate practices that can be considered specific examples of the broader autoregulation training framework. A primary limitation of previous research includes inconsistent use of key terminology (e.g., adaptation, readiness, fatigue, and response) and associated ambiguity of how to implement different autoregulation strategies. Crucially, this ambiguity in terminology and failure to provide a holistic overview of autoregulation limits the synthesis of existing research findings and their dissemination to practitioners working in both performance and health contexts. Therefore, the purpose of the current review was threefold: first, we provide a broad overview of various autoregulation strategies and their development in both research and practice whilst highlighting the inconsistencies in definitions and terminology that currently exist. Second, we present an overarching conceptual framework that can be used to generate operational definitions and contextualise autoregulation within broader training theory. Finally, we show how previous definitions of autoregulation fit within the proposed framework and provide specific examples of how common practices may be viewed, highlighting their individual subtleties.