Introduction
While the benefits of regular exercise are well-documented, sustaining engagement over time can be challenging (Ibrahim etat., 2024). The individual response to exercise can have large variations owing to different recovery rates (McLester et al., 2003), training age (Baker, 2013) and different life stressors (Ibrahim et at., 2024). Recognizing this, the concept of autoregulation emerges as a pivotal strategy by adjusting workouts in response changing states to daily physiological and psychological readiness (Ibrahim et at., 2024). While numerous strategies exist in a broader context like heart rate variability (HRV), within session strategies consist of rate of perceived exertion (RPE) and velocity-based training (Ibrahim et at., 2024) seem to be the most common.
Developed by Gunnar Borg, the Borg 15-point scale, which ranges from 6 to 20, along with the Borg Category-Ratio (CR-10) scale, ranging from 1 to 10, were both initially developed to gauge the self-perceived intensity of aerobic exercise (Ormsbee et al., 2019). With these scales, lower scores denote less perceived effort, with higher scores representing higher effort. While these scales have gained traction in research, an adapted version of the Borg Scale was developed and popularised within the powerlifting community, by Mike Tuchscherer (Zourdos et al., 2016). This modified approach diverges from the traditional Borg scale by evaluating exercise intensity by repetitions in reserve (RIR) at the end of a set, rather than a general sense of exertion. The utility of prescribing intensities based off RIR-RPE, is that intra-session sets can be auto-adjusted based on the individual response to the previous set (Ormsbeeet al., 2019) which may be more applicable in the weightlifting community. Additionally, the scale gives an indication as to the next action to take to maintain an appropriate stimulus. This nuance allows for a more specific assessment relevant to resistance training.
Research indicates that experienced lifters demonstrate a higher accuracy in estimating the number of reps they can perform before reaching failure, compared to utilizingtraditional category-based Borg ratings (Hackett, 2012). This suggests RIR might resonate more closely with experienced lifters, possibly because of intuitively self-assessing reps in a remaining set more often. In support of this statement, Ormsbee et al. (2019), found novice benchers (4 out of 13) are less able at selecting an RPE >9.5 at their 1-repetition maximum compared to their experienced counterparts, all of which (14 out of 14) selected an RPE >9.5, suggesting a learning component to RPE. While this may be seen as a drawback for novices, it is an indication that with experience lifters can better self-select weight at competitions, which is a vital component to performing well at competition. On the other hand, novices may benefit from incorporating fixed percentages of 1RM to ground self-assessments of intensity as they tend to underestimate hypertrophic intensities (Glass et al., 2004). Indeed, this structured approach can help build the foundational experience necessary for more accurate self-assessment of effort and capacity in and out of competitions.
However, prescribing exercise intensities based off RIR-RPEor the traditional RPE scale is not without inherent issues. According to Greget al. (2020), there is no method validated through research that can autoregulate daily exercise volume. Although not a validated method, once a lifter reaches their target RIR-RPE for a top set, they could decrease the weight and continue to perform sets until the target RIR-RPE is hit again thereby autoregulating exercise volume to some degree. Alternatively, a lifter could simply continue performing additional sets until they are unable to attain the previous reps of the prior set or achieve a target rep range. Another inherent issue with RIR-RPE based prescription is that this method has gained little traction in research resulting in erroneous conversion tables claiming to be based of the Mike Tuchscherer RIR-RPE model leading to confusion between practitioners. Nevertheless, RIR-RPE proves to not only be a valuable method is autoregulating intra-set intensities but also as a valuable skill to learn for competitions.
An alternative to using subjective rating systems is to use an objective rating system. One such objective method is called Velocity-Based Training (VBT) which measures the velocity of barbell as a surrogate for relative intensity (Ormsbee et al., 2019). The premise is based on the inverse relationship between the absolute weight and concentric barbell velocity (Ormsbee et al., 2019). Therefore, as the lifter gradually approaches their daily 1RM there is a corresponding decrease in barbell velocity (Ormsbee et al., 2019). Consequently, based on the theoretical load velocity relationship, loads lifted at lower intensities with maximal intent can predict a lifters absolute weight at higher intensities.
As shown by Banyard et al.(2017), the load-velocity relationship has a strong reliability to predict 1RM back squat based on the high intraclass correlation coefficient (ICC). Specifically, the actual 1RM had an ICC of 0.99, with predicted 1RMs showing decreasing reliability at lower percentages of 1RM (90%=0.92, 80%=0.87, 60%=0.72). This indicates a consistent ability to replicate results across sessions, although the reliability of the load-velocity predictions declines with lower 1RM percentages. However, the actual 1RM and predicted 1RM were significantly different at 90% (−5.5 to 27.8%), 80% (−12.3 to 29.4%), and 60% (−5.5 to 47.6%). The significant difference suggests that while predictions were reliable, they did not accurately reflect the true 1RM of lifters. Additionally, the standard error estimate for 90%, 80% and 60% revealed a large error margin of 10.6, 12.9, 17.2kg, respectively. Collectively, this study suggests a high reliability but low validity in predicting 1RM back squat, especially at lower intensities.
The study points out apossible divergence in applying theory to practice. While the relationship may exist on a theoretical level, in practical terms lifters may decelerate when lifting at lower intensities and thus affecting the calculation (Banyard etal., 2017). The other factor to consider is the amount the individual lifter uses the stretch-shortening cycle during the exercise (Banyard et al., 2017). Therefore,using free weights, machine-based exercises and the inclusion of pauses or banded exercises introduce variables which have an unknown effect on this relationship. Consequently, there a myriad of factors that could decrease the validity of 1RM estimates and the transferability to derivative exercises which partly negates the purpose of using VBT as an autoregulatory method.
Perhaps, a more pragmatic application of VBT is to use it to build an individualised load-velocity relationship with load adjusted to maintain an average velocity range that corresponds to a percentage of 1RM (Greget al., 2020). In contrast to RIR-RPE, VBT seems more able to autoregulatedaily exercise volume with use of stopping velocities in either absolute or percentage terms (Greg et al., 2020). Pareja-Blanco et al. (2017) found ending a set once there was a 20% reduction in set velocity or a 40% reduction in the first rep velocity resulted in either greater hypertrophy or greater strength and power respectively. Additionally, when compared to using RIR-RPE, VBT may prove to be a valuable within session feedback mechanism as it has shown to increase performance in the countermovement jump, squat, and bench by more than double in semi-pro rugby players (Shattock & Tee, 2022). Collectively, these studies suggest VBT to have some utility irrespective of the low validity predicting 1RM and likely to be more effective when used in a more individual nuanced approach.
Firstly, I want to go on the record and say though I have not read about anyone proposing a similar method I am almost certain that this is not an original idea. The basic premise of this method is that you quantify the performance decrement from a top set to another set as a percentage. As an example, after benching 70kg for 10 reps I had to drop the weight to 60kg to meet my target reps. This means that my performance decrement from the top set to the next working set within my target rep range is about 14%. So, if I increase the performance of my bench next session, by weight, reps or some sought of pause, then I now have a metric that suggests my overload threshold is about a 14% performance decrement.
This method may also be able to autoregulate volume as once I hit my overload threshold for an exercise, I just move on to the next exercise even if it has occurred a bit earlier than previous sessions. Therefore, knowing your overload threshold may prevent you from performing excessive junk volume. Another potential advantage I see using this method over the previous methods is that it may give you an earlier indication when an exercise has reached the accommodation phase, or a poor time: benefit ratio. Basically, the accommodation phase is when you have grown overly accustomed to the exercise and it is starting to yield relatively less benefit than others and it may be time to switch it up to achieve a higher performance decrement. So, when you notice the overload threshold trending lower over the weeks, despite increasing volume, it may be time to make some adjustments.
The integration of autoregulation strategies into training programs represents a significant advancement in personalized and adaptive exercise prescription. Both RIR-RPE and VBT offer distinct approaches to autoregulation, each with its own strengths and limitations.
RIR-RPE provides a subjective measure of exercise intensity, allowing for intra-session adjustments based on perceived exertion and remaining repetitions. Its utility lies in its ability to customize training loads dynamically, promoting an individualized approach to intensity regulation. However, its reliance on subjective assessment and the potential for inaccurate self-reporting, particularly among novices, underscores the need for caution and education when applied as sole means of regulation. The method's lack of validation for regulating daily exercise volume further highlights the necessity for more research and development of clear guidelines to optimize its use in training. On the other hand, VBT offers an objective measure of training intensity through the monitoring of barbell velocity, providing direct feedback and a quantifiable approach to autoregulate intensity. The inverse relationship between load and velocity underpins a reliable method to predict 1RM but a poor validity in predicting 1RM, especially at lower intensities. Therefore, using VBT requires careful consideration of the individual differences in lifting technique and the influence of the stretch-shortening cycle.
Both methods demonstrate thepotential to enhance training outcomes through tailored adjustments to training loads, acknowledging individual variability in response to exercise. However, the successful implementation of either method requires a nuanced understanding of its principles, potential drawbacks, and the context in which it is applied. Ultimately, the choice between RIR-RPE and VBT should be guided by the specific goals of the training program, the experience level of the athlete, and the availability of resources to accurately measure performance metrics. As research continues to evolve, these autoregulation strategies are poised to play a pivotal role in optimizing training efficiency, enhancing athletic performance, and minimizing the risk of overtraining.
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