Effects of velocity-based training vs. alternative resistance training on adaptations in maximal strength, power and sprint speed: a systematic review and meta-analysis
Creators: name: Orange, Samuel
name: Steele, James
name: Jones, Thomas
name: Pearson, Liam
name: jeffries, owen
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Sports Sciences
subject: Kinesiology
subject: Exercise Science
subject: Physiology
subject: FOS: Biological sciences
subject: Exercise Physiology
subject: Medicine and Health Sciences
subject: Life Sciences
subject: Rehabilitation and Therapy
subject: Other Kinesiology

description: Effects of velocity-based training vs. alternative resistance training methods on adaptations in maximal strength, power and sprint speed: a systematic review and meta-analysis Pre-registration based on PROSPERO international prospective register of systematic reviews Date: 22/05/2021 1. Review title. Effects of velocity-based training vs. alternative resistance training methods on adaptations in maximal strength, power and sprint speed: a systematic review and meta-analysis 2. Original language title. English 3. Anticipated or actual start date. 24/05/2021 4. Anticipated completion date. November 2021 5. Stage of review at time of this submission. The review has not yet started and thus we have not started any preliminary searches, piloting of the selection process, formal screening of search results against eligibility criteria, data extraction, risk of bias (quality) assessment, or data analysis 6. Named contact. Samuel T. Orange 7. Named contact email. sam.orange@newcastle.ac.uk 8. Named contact address. School of Biomedical, Nutritional, and Sport Sciences, Faculty of Medical Sciences, Newcastle University Centre for Cancer, Newcastle University, Newcastle upon Tyne, UK. 9. Named contact phone number. +44 (0)191 208 0570 10. Organizational affiliation of the review. School of Biomedical, Nutritional, and Sport Sciences, Faculty of Medical Sciences, Newcastle University Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Faculty of Sport, Health and Social Sciences, Solent University 11. Review team members and their organizational affiliations. Sam Orange, Newcastle University, UK James Steele, Solent University, UK Owen Jeffries, Newcastle University, UK Thomas Jones, Northumbria University, UK Liam Pearson, Northumbria University, UK Adam Hritz, Newcastle University, UK 12. Funding sources/sponsors. There are no sources of funding for this systematic review. 13. Conflicts of interest. There are no actual or perceived conflicts of interest (financial nor academic) 14. Collaborators. N/A 15. Review question. Is there a difference between the effects of velocity-based training and alternative resistance training methods on measures of maximal strength, power, or sprint speed? 16. Searches. We will conduct an electronic literature search of PubMed, Embase, SportDiscus, Cochrane Central Register of Controlled Trials (CENTRAL), ClinicalTrials.gov, International Standard Randomised Controlled Trial Number (ISRCTN) and SportRxiv databases. 17. URL to search strategy. The following search terms will be used: [All fields] “velocity-based training” OR “velocity training” OR “load velocity profile*” OR “velocity loss” OR “VBT” OR “autoregulation” OR “velocity training” AND [All fields] “resistance training” OR “strength training” OR “weight training” OR “weightlifting” AND [All fields] “one repetition maximum” OR 1RM OR “isometric” OR “isokinetic” OR speed OR sprint OR jump OR power 18. Condition or domain being studied. This review will focus using velocity feedback to regulate the prescription of resistance training load and/or volume. 19. Participants/population. Inclusion: Healthy participants aged ≥16 years Exclusion: Participants recruited on the basis of any medical condition 20. Intervention(s), exposure(s). Velocity-based training intervention lasting ≥4 weeks. Velocity-based training is defined as a resistance training intervention where velocity feedback is used to manipulate resistance training load or the number of repetitions/sets performed in at least one resistance exercise. Movement velocity must have been measured objectively using a measurement tool such as high-speed video capture, linear position/velocity transducer, inertial measurement unit, or laser optic device. 21. Comparator(s)/control. An intervention that did not use velocity feedback to manipulate resistance training load/volume, but instead informed resistance training prescription based on one of the following methods: i) a percentage of one repetition maximum that was assessed at baseline, ii) rating of perceived exertion (RPE), iii) repetitions in reserve, or iv) repetition maximum, which involves performing repetitions until concentric muscular failure. 22. Types of study to be included. Inclusion: Parallel group, randomized trials using a between-group design, or within-participant trials where contralateral limbs were randomised in parallel to the interventions Exclusion: Quasi-experimental, observational, or cross-over studies. In line with Cochrane guidelines, quasi-experimental trials will be excluded because it is feasible to conduct randomised trials to answer the questions being addressed by this review [1]. Crossover trials are considered inappropriate due to the likelihood of a carry-over effect [2]. 23. Context. N/A 24. Main outcome(s). Outcomes will include measures of maximal strength, power, and sprint speed and will be continuous variables. Maximal strength outcomes will include mass lifted in dynamic 1RM tests in the upper- or lower-body (e.g. back squat, bench press), or force achieved in isometric assessments (e.g. mid-thigh pull, squat, knee extension). Muscle power outcomes will include power or velocity obtained in the concentric phase of a resistance exercise (e.g. back squat). Muscle power outcomes could also include power produced or height achieved in a countermovement jump or depth jump. Sprint speed will include a timed maximal sprint between 5 and 100 m in distance. Measures of effect Mean difference, calculated as the mean pre-post change in the treatment group minus the mean pre-post change in the comparison group. If change scores are not available, mean differences will be calculated as the post-intervention score in the treatment group minus the post-intervention value in the comparison group. 25. Additional outcome(s). N/A 26. Data extraction (selection and coding). After the literature searches are complete, studies will be collected into a single list in a Microsoft Excel spreadsheet (Microsoft Corporation, Redmond, Washington, USA). Two authors will independently remove duplicates and screen the titles and abstracts to identify potentially eligible studies. Full-texts will be obtained for all studies that appear relevant or where there is any uncertainty. Two authors will then independently examine each full-text manuscript against the eligibility criteria. Any disagreements will be resolved through discussion and consensus. If it is necessary to clarify aspects of the study in relation to the eligibility criteria, or retrieve a full-text manuscript, we will contact corresponding authors on two occasions within a one-month period. Data items to be extracted from each eligible study include: i) authors, ii) title and year of publication, iii) sample size, iv) participant characteristics, v) details of the VBT intervention, vi) details of the comparison intervention, vii) details of the outcome measure(s), viii) details of adverse events, retention rates, and intervention adherence, ix) baseline, follow-up, change score data for each outcome measure (mean and SD). If SDs aren’t reported, we will collect other relevant data that can be converted to an SD, such as 95% confidence intervals (CIs) or p-values. Study authors will be contacted to obtain missing or unpublished data wherever necessary. If data are only presented graphically and we do not receive a response from study authors, we will use a web‐based digitizing tool to extract the data from graphs (WebPlotDigitizer) [3]. All relevant outcomes will be extracted and the multiplicity will be accounted for in the meta-analysis model. Two authors will independently extract all data items, which will be tabulated in a custom-designed data collection form on Microsoft Excel. Review authors will cross-check coding sheets and any conflicts between the reviewers will be resolved in consensus meetings. 27. Risk of bias (quality) assessment. The Cochrane risk of bias tool for randomized trials (RoB 2) will be used to judge the risk of bias for each included outcome within each study [4]. RoB 2 comprises five domains and a series of signalling questions relating to the: 1) randomisation process, 2) deviations from intended interventions, 3) missing outcome data, 4) measurement of the outcome, and 5) selection of the reported result. Judgements for each domain and the overall risk of bias are expressed as ‘low’, ‘high’, or ‘some concerns’. An overall bias judgment will be taken as the least favourable assessment across all the domains. Judgements will be made independently by two authors, with any disagreements resolved by discussion and consensus. Risk of bias due to missing results in a synthesis will be explored with Egger’s test of the intercept [5] and by visually inspecting a funnel plot of the treatment effects plotted against their corresponding sampling variance. We will rate the certainty of evidence for each outcome using the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) approach [6]. GRADE has four levels of evidence: very low, low, moderate and high. Randomised trials start with a ‘high’ rating, and we will downgrade the certainty evidence for each outcome based on the following factors: 1) risk of bias, 2) inconsistency of results, 3) indirectness of evidence, 4) imprecision of results, and 5) publication bias [7]. The evidence will be downgraded by one level if we judge that there is a serious limitation or by two levels if we judge there to be a very serious limitation. Criteria for downgrading the evidence are presented in the Appendix. Two review authors will independently grade the certainty of evidence, with any discrepancies resolved through consensus. An overall GRADE certainty rating will be applied to the body of evidence by taking the lowest certainty of evidence from all of the outcomes [8]. 28. Strategy for data synthesis. All eligible studies included in the review will be narratively synthesized. When two or more studies report the same outcome using the same measurement scale, we will perform a meta-analysis of mean differences between intervention groups. Mean differences will be calculated using the change score in each group (mean change from baseline to post-intervention), or the post-intervention score if the change score is not available. If the same measurement scale is not used, we will pool standardised mean differences (SMDs), which will be calculated by dividing the mean difference by the pooled SD at baseline [9]. Hedges' g correction will be applied to the SMD to adjust for sample bias. Qualitative descriptors used to interpret the strength of the SMDs will be based on Cohen’s criteria [10] (±): trivial (&lt; 0.2), small (0.2 to 0.49), moderate (0.5 to 0.79), and large (≥ 0.8). Meta-analyses will be performed with a random effects model using the restricted maximum likelihood method to estimate between-study variance. CIs and test statistics will be calculated via a t-distribution using the Hartung-Knapp-Sidik-Jonkman (HKSJ) approach. The HKSJ adjustment for random effects meta-analysis results in superior error rates when the number of included studies is small [11]. Studies will be weighted according to the inverse of the sampling variance. If a meta-analysis includes more than one outcome measure from the same study, effect estimates will be nested within studies using a multi-level structure to account for correlated effects [12]. Statistical heterogeneity between studies will be evaluated with the Chi-squared test (χ2), and the proportion of variability in effect estimates due to heterogeneity rather than sampling error will be estimated using the I2 statistic. Thresholds for the interpretation of I2 will be in line with Cochrane recommendations: 0-40% (‘might not be important’), 30-60% (‘may represent moderate heterogeneity’), 50-90% (‘may represent substantial heterogeneity’), and 75-100% (‘considerable heterogeneity’) [13]. The importance of the observed I2 value will be interpreted alongside its 95% CI and the p-value from the χ2 test [13]. We will use Graphic Display of Heterogeneity (GOSH) plots to investigate between-study heterogeneity and identify potentially influential studies. Statistical analyses will be conducted using package metafor in R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria). Statistical significance will be set at p&lt;0.05. Data will be presented as effect estimates with their corresponding 95% CIs. The search results, dataset, and statistical code will be made available on Open Science Framework. 29. Analysis of subgroups or subsets. If a meta-analysis model includes at least 10 effect estimates [13], we will explore sources of heterogeneity using exploratory random-effects meta-regressions. Covariates will include characteristics related to participants (age and resistance training experience) and the intervention (exercise selection, frequency, duration, load, volume, matching of total work). We will perform various sensitivity analyses to examine whether decisions made in the review process influence the overall findings. Sensitivity analyses will include, but are not limited to: i) including quasi-experimental trials, ii) computing test statistics and 95% CIs based on a standard normal distribution rather than a t-distribution, iii) using the post-intervention score rather than the change score to calculate MDs, and iv) using the change score SD to calculate effect estimates rather than the SD at baseline. 30. Type and method of review. Systematic review with meta-analysis of intervention studies 31. Language. English 32. Country. United Kingdom 33. Other registration details. N/A 34. Reference and/or URL for published protocol. N/A 35. Dissemination plans. A paper that presents the findings of this review will be submitted to a preprint server (https://www.sportrxiv.org/) and also to a peer-reviewed journal in the field of exercise science. We will also disseminate a summary of the findings via outlets on social media (e.g. via tweets from authors’ Twitter accounts). 36. Keywords. Systematic review; meta-analysis; muscle strength; resistance training; physical conditioning; velocity-based training 37. Details of any existing review of the same topic by the same authors. N/A 38. Current review status. Ongoing 39. Any additional information. No 40. Details of final report/publication(s) or preprints if available. N/A APPENDIX GRADE criteria for evaluating certainty of the evidence (see https://osf.io/wejn8/) References 1. McKenzie J, Brennan SE, Ryan RE, Thomson HJ, Johnston RV, Thomas J. Chapter 3: Defining the criteria for including studies and how they will be grouped for the synthesis. In: Cochrane Handbook for Systematic Reviews of Interventions. Accessed May 11, 2021. /handbook/current/chapter-03 2. Higgins JPT, Eldridge SM, Li T. Chapter 23: Including variants on randomized trials. In: Cochrane Handbook for Systematic Reviews of Interventions Version 6.2 (Updated February 2021). ; 2021. Accessed April 11, 2021. /handbook/current/chapter-23 3. Drevon D, Fursa SR, Malcolm AL. Intercoder Reliability and Validity of WebPlotDigitizer in Extracting Graphed Data. Behav Modif. 2017;41(2):323-339. doi:10.1177/0145445516673998 4. Higgins JPT, Savovic J, Page MJ, Elbers RG, Sterne JAC. Chapter 8: Assessing risk of bias in a randomized trial. In: Cochrane Handbook for Systematic Reviews of Interventions. 6th ed. Cochrane; 2019. https://training.cochrane.org/handbook/current/chapter-08 5. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629-634. doi:10.1136/bmj.315.7109.629 6. Guyatt GH, Oxman AD, Vist GE, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336(7650):924-926. doi:10.1136/bmj.39489.470347.AD 7. Schünemann HJ, Higgins JPT, Vist GE, et al. Chapter 14: Completing ‘Summary of findings’ tables and grading the certainty of the evidence. In: Cochrane Handbook for Systematic Reviews of Interventions. Cochrane; 2019. 8. Guyatt G, Oxman AD, Sultan S, et al. GRADE guidelines: 11. Making an overall rating of confidence in effect estimates for a single outcome and for all outcomes. J Clin Epidemiol. 2013;66(2):151-157. doi:10.1016/j.jclinepi.2012.01.006 9. Morris SB. Estimating Effect Sizes From Pretest-Posttest-Control Group Designs - Scott B. Morris, 2008. 2007;11(2):364-386. doi:doi.org/10.1177/1094428106291059 10. Cohen J. Statistical Power Analysis for the Behavioral Sciences. Routledge Academic; 1988. 11. IntHout J, Ioannidis JPA, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med Res Methodol. 2014;14:25. doi:10.1186/1471-2288-14-25 12. Van den Noortgate W, López-López JA, Marín-Martínez F, Sánchez-Meca J. Three-level meta-analysis of dependent effect sizes. Behav Res Methods. 2013;45(2):576-594. doi:10.3758/s13428-012-0261-6 13. Deeks JJ, Higgins JPT, Altman DG. Chapter 10: Analysing data and undertaking meta-analyses. In: Cochrane Handbook for Systematic Reviews of Interventions Version 6.2 (Updated February 2021). Vol 6.2. Cochrane; 2021. https://training.cochrane.org/handbook/current/chapter-10#section-10-10-2