The analysis suggests that hypertrophy improves as proximity to failure increases, but other factors such as load and exercise selection also play a role in program design.
Proximity to failure at a given load does not have a meaningful impact on strength gains, as specificity of load is the primary driver.
The relationship between proximity to failure and hypertrophy follows a non-linear pattern, but caution should be exercised in interpreting specific Reps in Reserve (RIR) values.
Various factors, such as set and repetition volume equating, biological age, and training frequency, can modify the impact of proximity to failure on hypertrophy, highlighting the need for individualized program design.
Deep dives
Hypertrophy findings
The analysis indicates that hypertrophy improves as proximity to failure increases, with the best fit model showing a non-linear relationship. However, it's important to consider the limitations of the estimations made for proximity to failure and the uncertainty associated with the best fit curve. While the relationship is robust, the magnitude of the effect and the specific RIR values should not be overinterpreted. Positive outcomes can still be seen even at higher RIRs, indicating that proximity to failure is not the sole determining factor for hypertrophy. Other factors such as load, exercise selection, and individual differences must also be considered in program design.
Strength findings
For strength, the analysis suggests that proximity to failure at a given load does not have a meaningful impact on strength gains. The relationship between proximity to failure and strength is negligible and does not show a consistent trend. Specificity, or the load on the bar, seems to be the primary driver of strength gains. It's important to note that these findings are specific to the analyzed studies and the load ranges examined.
Best model fit
The best fit model for hypertrophy demonstrates a non-linear relationship, where improvements in hypertrophy accelerate as proximity to failure increases. However, this model should be viewed with caution due to the many assumptions and limitations of proximity to failure estimations. The linear model provides an alternative perspective for interpreting the relationship. It's crucial to consider the uncertainty intervals and the overall directionality of the effect rather than focusing on specific RIR values.
Moderators impacting hypertrophy
Various factors influence the relationship between proximity to failure and hypertrophy. These include set and repetition volume equating, biological age, exercise selection, progressive overload, intervention duration, participant gender, training frequency, and within-participant designs. These moderators can modify the impact of proximity to failure on hypertrophy and highlight the interdependent nature of training variables when considering optimal program design.
Uncertainty and Limitations
The podcast episode explores the uncertainty and limitations of the analysis conducted in terms of interpreting the results. It emphasizes that the analysis was exploratory and not a systematic review, and that the estimation of Reps in Reserve (RIR) involved subjective decisions and compromises. The results should be contextualized and not overinterpreted, acknowledging the potential changes that could occur during the peer review process and with more accurate RIR estimations. It also notes that the findings provide a framework for decision-making and optimization on an individual level, but the application should consider the specific population, length of interventions, and personal experiences.
The Relationship between Failure Training and Adaptations
The podcast discusses the relationship between training to failure and adaptations, focusing on the effects on hypertrophy and strength. It highlights that the analysis indicates a possible benefit of training closer to failure for hypertrophy, while for strength, there is no significant difference overall, except in specific cases such as multi-joint movements or studies where volume and repetition were equated. It emphasizes the need to consider the context and population for applying the results, and the importance of individualized decision-making based on personal experiences and interpretations of the research.
Exploring the Sustainability of Failure Training
The podcast explores the sustainability of training to failure, challenging the assumption that failure leads to greater fatigue and negative impacts on long-term adaptations. It references studies that suggest an adaptation to the repetitive bout effect, where individuals can adapt to the fatigue caused by failure training over time. The discussion highlights that while performance may be affected, it does not necessarily negatively impact hypertrophy. The concept of performance decrements and their relevance to the productivity of the sets are explored, suggesting that a certain degree of performance decrement may not be detrimental to adaptations.
Optimization and Individualization
The podcast emphasizes the importance of optimization and individualization in training strategies, considering factors such as volume, frequency, exercise selection, and proximity to failure. It clarifies that the analysis does not dictate that one factor is more important than another and that the decision-making process should take into account personal experiences, population characteristics, and specific goals. It encourages integrating different training approaches and strategically adjusting various variables to achieve optimal adaptations on an individual basis.
In this MASScast episode, we’re joined not only by MASScast fellow host Dr. Mike Zourdos but also Ph.D. candidate Zac Robinson of Data Driven Strength, both of whom are co-authors on a pre-print that is making a lot of waves in the evidence-based community. Their series of meta-regressions has shown that perhaps the impact of failure is more substantial than previously believed, at least for hypertrophy. But is it that simple? Also, is this paper a “game changer” when it comes to the understanding how volume and effort relate to hypertrophy? Tune in to find out the details that almost everyone talking about this paper, besides the authors themselves, are getting wrong.
Refalo 2023 Influence of Resistance Training Proximity-to-Failure on Skeletal Muscle Hypertrophy: A Systematic Review with Meta-analysis https://pubmed.ncbi.nlm.nih.gov/36334240/
19:35 Estimating RIR from alternative set structures (cluster and rest redistribution sets) and velocity-based training
Carroll 2019 Skeletal Muscle Fiber Adaptations Following Resistance Training Using Repetition Maximums or Relative Intensity https://pubmed.ncbi.nlm.nih.gov/31373325/
39:00 Going in-depth on the meta regression models and how resistance training variables interact with each other
Schoenfeld 2017 Dose-response relationship between weekly resistance training volume and increases in muscle mass: A systematic review and meta-analysis https://pubmed.ncbi.nlm.nih.gov/27433992/
