Background: Understanding the features important for performance in non-disabled bench press and Paralympic powerlifting may help inform talent identification and transfer models. The aim of this scoping review was to describe features associated with bench press performance.
Methods: We conducted a systematic search of three electronic databases (PubMed, SportDiscus and EMBASE) to identify studies involving non-disabled and Para athlete populations that investigated features related to bench press one-repetition maximum (1RM), across six domains. The domains were: anthropometric, body composition, technical, neuromuscular, demographic and disability. Studies using adult participants, with at least six-months of bench press experience, who were able to bench press their body mass were included. Narrative synthesis was used to describe the studies and features associated with bench press 1RM.
Results: Thirty-two studies met our inclusion criteria. Twenty-four studies involved non-disabled athletes (total n = 2,686; 21.9% female) and eight involved Para athletes (total n = 2,364; 39.4% female). The included studies explored 111 unique features across the six domains. Anthropometric and body composition features were most studied, with about half of the 32 studies investigating features from only a single domain. Anthropometric features, such as arm circumference and body mass, shared the strongest associations with bench press 1RM. There was a primary reliance on bivariate correlation analysis (56% of studies), with few studies considering the uncertainty of their results. Practices of open and transparent science, such as pre-registration and data sharing, were absent.
Conclusion: The development of bench press talent identification and sport transfer models will require future studies to investigate both non-training and training modifiable features, across multiple domains. Features should be combined using multivariable model approaches, that consider confounding and the potential for modifying effects. Large, longitudinal studies that use information from athlete monitoring databases are needed to better understand the specific features associated with bench press performance, and for the development of talent identification and sport transfer models.
With the R packages here, readxl, janitor, dplyr, tidyr, binom, ggplot2, ggupset, viridis, cowplot, grid, and gridExtra, the results and figures presented in the scoping review can be replicated using the data files and code stored in this repository.
