Lifting velocity as a predictor of intensity and level of effort during the prone bench pull exercise

Abstract

Resistance training (RT) has long been recognized as a cornerstone not only for athletic performance but also for overall health and well-being. Beyond enhancing muscle size, strength, and power, RT plays a vital role in preventing and managing a wide range of health conditions. However, the success of RT in inducing these positive outcomes relies heavily on the careful manipulation of training variables (e.g., exercise selection, load intensity, volume, rest intervals, and lifting velocity). A common issue faced by coaches and athletes is determining the appropriate weight to lift in a specific exercise, as the intensity of the resistance directly influences the degree of adaptation. Two of the most frequently used methods for prescribing training intensity involve assigning a load relative to the individual’s maximum strength capacity (i.e., %1RM) or determining the load that allows a specific number of repetitions before reaching muscular failure (i.e., 7RM represents the maximum weight with which an individual can complete seven repetitions before failure; XRM). However, traditional approaches to assessing 1RM and XRM are often criticized for being time-intensive, physically exhausting, and mentally demanding. Velocity-based training (VBT) has emerged as a modern, objective, and auto-regulatory approach to resistance training. Due to its methodological robustness and feasibility within an athlete's daily routine, one promising method for predicting XRM is through monitoring lifting velocity at maximal concentric effort during submaximal loads. This novel VBT’s application establishes an individualized linear relationship between the maximum repetitions to momentary failure (RTF) and maximum velocity of the set Vfastest (i.e., individualized RTF-Vfastest relationships). Then, it uses this data to predict different RTFs in subsequent sessions based on the specific training objectives. From a biomechanical and training perspective, the present thesis aims (1) to determine the basic properties of the RTF-Vfastest relationship, such as goodness-of-fit, reliability, and accuracy (i.e., error in RTF prediction), and (2) to offer guidance on implementing various methodological factors that can impact the accuracy of RTF prediction, including the magnitude of loads lifted, the number of loads, and the specific lifting velocity variable considered. Of note, conducting methodological studies provide a solid foundation for improving and optimizing the techniques and tools used in future research, ensuring consistent advancements in knowledge and technology. The thesis’ results suggest that the individualized RTF-Vfastest relationships demonstrate: (I) a higher goodness-of-fit compared to generalized models which remains stable over time, (II) a range from acceptable to high between-session reliability for Vfastest values associated with specific RTFs, (III) a high stability over time for Vfastest values associated with specific RTFs and, (IV) an acceptable RTF prediction accuracy under free-fatigue conditions. Complementary, the basic properties from individualized RTF-Vfastest relationships are extrapolated to different equipment (e.g., Smith machine or free-weight), lifting velocity variables (e.g., fastest mean or peak velocity within a set), magnitude of the loads analyzed (from 60% to 90%1RM), number of sets (from 2 to 4 sets), and resting time (from 5 to 10 minutes) used for the equation’s construction. From a practical standpoint, RTF-Vfastest relationships can be constructed using a simple linear regression model by executing sets to failure with varying loads (from 2 to 4 sets). This approach requires the monitoring of two variables for the modelling: (i) RTF for each set and, (ii) Vfastest within each set. Once established, coaches simply need to measure the Vfastest against a given load (typically occurring in the first 1-3 repetitions). Then, this velocity can be inserted the individualized equation for obtaining the RTF prediction in real-time. Finally, readers should know that nowadays, monitoring lifting velocity can be easily done with affordable and accessible devices, making it feasible for use in any sports context.