Date of Award
Doctor of Philosophy
School of Medical and Health Sciences
Professor Kazunori Nosaka
Dr James Tufano
Field of Research Code
110602, 110299, 11999
Traditionally, resistance training has been prescribed using percent-based training (PBT) methods that use the loads relative to a maximal load lifted for one repetition (1RM). However, PBT does not take into account possible day-to-day fluctuations in performance that may occur from physical or psychological stressors. One approach to address this limitation is to monitor velocity changes during resistance training, based on research showing that declines in velocity are highly correlated with fatigue. Therefore, velocity-based training (VBT) methods are proposed to provide a more objective method to modify resistance training sessions based on individual differences in day-to-day performance and the rate of training adaptation. However, at the commencement of this dissertation in 2014, no previous research had examined VBT methods in comparison to PBT methods. Thus, this thesis aimed to verify the efficacy of different VBT methods using a resistance-trained population who could lift a minimum of 150% their own body mass for at least one repetition in the full-depth back squat. These parameters were chosen so that the findings of this research were applicable to strength-trained athletes who were likely to employ VBT methods in their resistance training programs.
In the first of five research studies, two portable VBT devices were examined for their accuracy to assess peak velocity (PV) and mean velocity (MV) among other kinematic variables. On three separate days, ten strength-trained men performed three 1RM back squat trials that comprised loads of 20%, 40%, 60%, 80%, 90% and 100% of 1RM. Acceptable validity criteria was based on a Pearson moment correlation coefficient >0.70, coefficient of variation (CV) ≤10% and Cohen d effect size (ES) r = 0.94 – 0.97, CV = 2.9 – 5.8%) and MV (r = 0.95 – 0.99, CV = 3.2 – 4.5%) across the relative load spectrum when compared to laboratory testing equipment. Thus, for the remainder of the VBT studies in this PhD thesis project, an LT was used to report the velocity data.
In the second study, a novel velocity-based load monitoring method was investigated using 17 strength-trained men who performed three 1RM trials on separate days. Specifically, the reliability and validity of the load-velocity relationship to predict the back squat 1RM was calculated by entering MV at 100% 1RM into individualised linear regression equations which were derived from the load-velocity relationship of three (20%, 40%, 60% of 1RM), four (20%, 40%, 60%, 80% 1RM), or five (20%, 40%, 60%, 80%, 90% 1RM) incremental warm-up sets. The results showed that this predicted 1RM method was moderately reliable (ICC = 0.72 – 0.92, CV = 7.4 – 12.8%), and moderately valid (r = 0.78 – 0.93, CV = 5.7 – 12.2%). However, it could not be used as a VBT method to accurately modify training loads, since it significantly over-predicted the actual 1RM (SEE = 10.6 – 17.2 kg) due to the large variability of MV at 100% 1RM (ICC = 0.42, SEM = 0.05 m·s-1, CV = 22.5%). Therefore, this 1RM prediction method was no longer utilised as a method of adjusting training load for the remainder of the project.
Despite its suggested importance, research had yet to investigate if velocity was stable between training sessions, so that individualised load-velocity profiles (LVP) could be created to track changes in velocity. Thus, the third study attempted to fill this research gap, where 18 strength-trained men performed three 1RM trials, which included warm-up loads pertaining to 20%, 40%, 60%, 80%, 90% and 100% 1RM, with the velocity of each repetition assessed by LT. It was found that PV, mean propulsive velocity (MPV) and MV were all reliable (ICC > 0.70, CV ≤ 10%, ES < 0.6) for the back squat performed at 20%, 40%, 60%, 80%, and 90% 1RM but not at 100% 1RM for MPV and MV. This meant that all three concentric velocity types could be used to develop LVPs. In addition, the smallest detectable difference was established across the relative load spectrum for PV (0.11 – 0.19 m·s-1), MPV (0.08 – 0.11 m·s-1) and MV (0.06 – 0.11 m·s-1), which then allows coaches to determine meaningful changes in velocity from their athletes between training sessions. Collectively, these results showed that LVPs could be utilised as a VBT method for monitoring sessional changes in velocity and modifying resistance-training loads according to individual differences in day-to-day performance.
The fourth study compared the kinetic and kinematic data from three different VBT sessions and a PBT session in order to provide programmatic guidance to strength coaches who may choose to implement these novel methods to adjust resistance training load or volume. Fifteen strength-trained men performed four randomised resistance-training sessions 96 hours apart, which included a PBT session involving five sets of five repetitions at 80% 1RM, a LVP session (verified from Study 3) consisting of five sets of five repetitions with a load that could be adjusted to achieve a target velocity from an individualised LVP regression equation at 80% 1RM, a fixed sets 20% velocity loss threshold FSVL20 session that contained five sets at 80% of 1RM but sets were terminated once MV dropped below 20% of the maximal attainable MV from the first set or when five repetitions were completed, a variable sets 20% velocity loss threshold VSVL20 session that comprised 25 repetitions in total but participants performed as many repetitions in a set until the 20% velocity loss threshold was exceeded or 25 repetitions was completed. During the LVP and FSVL20 sessions, individuals performed repetitions with faster (p < 0.05) sessional MV (ES = 0.81 – 1.05) and PV (ES = 0.98 – 1.12), avoided additional mechanical stress with less time under tension but maintained similar force and power outputs when compared to the PBT session. Therefore, the LVP and FSVL20 methods could be employed in a strength-oriented training phase to diminish fatigue-induced decreases in movement velocity that can occur in PBT.
The VBT method employed in the fifth and final study was derived from the results of Study 4. Both the LVP and FSVL20 methods permitted faster repetition velocities throughout a training session compared to PBT, but it was decided that the FSVL20 method could decrease total training volume and reduce the training stimulus, which may be unwarranted. Therefore, in Study 5, the effects of the LVP-VBT approach (VBT) versus PBT on changes in strength, power and sports performance measures following six weeks of back squat training were examined. The study involved 24 strength-trained men who performed back squat training three times per week in a daily undulating format. The training protocols were matched for sets and repetitions but differed in the assigned training load. PBT group trained with relative loads varying from 59% – 85% 1RM, whereas the VBT group trained with loads that could be adjusted to achieve a target velocity from an individualised LVP that corresponded with 59% – 85% 1RM. Pre- and post-training assessments included 1RM, 30% of 1RM countermovement jump (CMJ), 20-m sprint, and 505 change of direction test (COD). Overall, the VBT group performed repetitions with faster velocities during training (p < 0.05, MV = 0.76 m·s-1 vs. 0.66 m·s-1) that were perceived as less difficult (p < 0.05, rating of perceived exertion = 5.1 vs. 6.0), and utilized marginally lower training loads (p < 0.05, ~1.7%1RM) compared to PBT. Both VBT and PBT methods were effective for significantly enhancing 1RM (VBT: 11.3% vs. PBT: 12.5%), CMJ peak power (VBT: 7.4% vs. PBT: 6.0%), 20-m sprint (VBT: -1.9% vs. PBT: -0.9%), and COD (VBT: -5.4% vs. PBT: -3.6%). No significant differences were observed between groups for any testing assessment but likely favourable training effects were observed in 1RM for PBT group, whilst VBT group had likely favourable improvements in 5-m sprint time, and possibly favourable improvements in 10-m sprint time, and COD time. These findings suggest that both VBT and PBT methods are similarly effective; however, PBT may provide a slight 1RM strength advantage whilst VBT may be preferred by some individuals, since it permits faster training velocities, is perceived as less difficult, and is a more objective method for adjusting training load to account for individual differences in the rate of training adaptation.
In conclusion, VBT (LVP approach) and PBT are similarly effective for promoting significant improvements in strength, power and sports performance tasks in strength-trained participants. However, even though the LVP-based VBT method did not provide significant increases in strength and power adaptations compared to PBT, it provided similar improvements while avoiding additional mechanical loading which may be important for the better management of training load, particularly with athletes who partake in numerous training modalities which can influence fatigue and recovery. That being said, if all repetitions are performed with maximal intended velocity but not to concentric muscular failure, a well-planned, periodised resistance training program with regular training frequency and progressive overload that accounts for bouts of recovery will provide adequate stimulus to significantly enhance strength, power and performance tasks like sprinting and changes in direction. Future training studies may look to examine the efficacy of VBT methods using multiple exercises (upper and lower body), and with different populations including women, adolescents, older adults, and potentially individuals during rehabilitation from injury so that training progress can be objectively monitored. Furthermore, future studies could look to incorporate multiple VBT methods into a training program such as the LVP method to modify resistance training load and the velocity loss thresholds method to control resistance training volume.
Access to Appendix L of this thesis is not available.
Banyard, H. G. (2019). Velocity-based training: Monitoring, implementation and effects on strength and power. https://ro.ecu.edu.au/theses/2164
Available for download on Thursday, February 04, 2021