The accuracy of load-velocity relationships to predict 1RM: a systematic review and individual participant data meta-analysis. [Dataset]

Abstract

The load lifted during resistance training is frequently prescribed in terms of a percentage of the maximum load that can be lifted for one repetition (1RM;González-Badillo and Sánchez-Medina 2010). This process allows for both individualisation of a training stimulus and prescription of various training zones based on the relative load lifted that can be used to target distinct physical qualities (Fleck and Kraemer 2014). Despite extensive research and practical experience supporting the use of 1RMs to prescribe resistance training, the process can also be viewed as inconvenient, time-consuming and limited by the precision of a single measurement that may fluctuate on a daily basis due to changes in readiness (Shattock and Tee 2020; Greig et al. [2020) or trend substantively over the short-to-medium term due to changes in fitness and fatigue (Dorrell, Smith and Gee 2019; Greig et al. 2020). Previous attempts to address limitations such as the time required to determine an individual's 1RM include repetition-maximum tests with a sub-maximum load that can then be used to predict 1RM (Pestaña-Melero et al. 2018). However, repeated administration of any repetition-maximum test is likely to generate undesirable levels of fatigue, thereby limiting the frequency with which the measurement process can be performed (Banyard, Nosaka and Haff 2017). More recently, alternative processes have been adopted to predict 1RM through the use of load-velocity relationships (Hughes et al. 2019). Underpinning these processes include a strong inverse linear relationships between load and velocity (González-Badillo and Sánchez-Medina 2010), and the recent proliferation of technologies that can accurately measure velocity during resistance training. The prediction of 1RM from load-velocity relationships represents an appealing alternative for practitioners, as the process does not require performance of a fatiguing repetition-maximum test, and can be completed at high frequencies including each resistance training session (Perez-Castilla et al. 2019). In addition, the process can be incorporated into pre-existing warm-up routines such that the prediction of daily 1RM requires no additional time to complete.

Citation

GREIG, L., ASPE, R.R., HALL, A., COMFORT, P., COOPER, K. and SWINTON, P.A. 2023. The accuracy of load-velocity relationships to predict 1RM: a systematic review and individual participant data meta-analysis. [Dataset]. Hosted on Open Science Framework (OSF) [online]. Available from: https://doi.org/10.17605/OSF.IO/6DXP5

• Online Publication Date: Jul 26, 2023
• Publication Date: Jul 26, 2023
• Deposit Date: Aug 1, 2023
• Publicly Available Date: Aug 1, 2023
• Publisher: OSF: Center for Open Science
• DOI: https://doi.org/10.17605/OSF.IO/6DXP5
• Keywords: Monitoring; Maximum strength; Velocity based training; Autoregulation
• Public URL: https://rgu-repository.worktribe.com/output/2028968
• Related Public URLs: https://rgu-repository.worktribe.com/output/1947429 (Journal article, published in Sports Medicine)
• Type of Data: Zip file containing: two XLSX files, three DOCX files, one PDF and two code for analsysis
• Collection Date: Feb 16, 2021
• Collection Method: This review was conducted in line with best practice guidelines for conducting systematic reviews, as outlined by JBI and a pre-registered protocol (https://osf.io/agpfm/). Items were reported according to the PRISMA-IPD, which is a PRISMA variant specifically designed for IPD meta-analyses. A completed version of the PRISMA-IPD checklist can be found in the supplementary materials (Online Resource 1). By selecting an IPD meta-analysis design, results from previous studies that were reported using diffuse statistics could be synthesised into common effect sizes both to facilitate further investigation and to enhance the interpretability of results.