Low vs. High-Load Resistance Training: Which is Best for Strength and Hypertrophy?

In the world of resistance training, the question of whether to lift heavy weights or opt for lighter loads is a subject of ongoing debate. The conventional wisdom dictates that lifting heavy weights, exceeding 70% of your one-repetition maximum (1RM), is the key to achieving maximal muscle strength and hypertrophy. This belief aligns with what's known as the "RM continuum," which suggests that different repetition ranges are optimal for distinct goals: 1-5RM for strength and 6-12RM for hypertrophy. The underlying theory here is that heavy loads are essential for engaging the highest threshold motor units, which drive significant muscle adaptations.

The Muscle Fiber Recruitment Puzzle

The central question in this debate revolves around whether lighter loads can effectively recruit the entire motor unit (MU) pool during a set of repetitions. Conventional wisdom aligns with the size principle, stating that smaller MUs are recruited initially, with larger MUs joining as force requirements increase. This concept supports the argument for heavy loads. However, some researchers challenge this, proposing that training with as low as 30% of 1RM can lead to complete MU recruitment, provided sets are carried out to the point of momentary muscular failure.

The sEMG Enigma

Surface electromyography (sEMG) studies have consistently shown lower mean electrical amplitudes when using loads below 50% of 1RM compared to heavier loads exceeding 70% 1RM, even when sets are performed to muscular failure. However, some research suggests that peak EMG amplitudes can be similar between different load ranges. The discrepancies in these findings might stem from variations in the methods used to analyze EMG data throughout a set leading to failure.

Deciphering Complex Factors Affecting sEMG

It's crucial to understand that sEMG amplitude is influenced by multiple factors, including recruitment, firing frequency, synchronization, propagation velocity, and intracellular action potentials. These factors can be affected by exercise-induced fatigue, further complicating the interpretation of the effects of loading intensity from EMG data. Additionally, some researchers suggest that MUs might cyclically de-recruit and re-recruit during a light-load set of repetitions to maintain force output, potentially altering sEMG amplitude. Importantly, sEMG amplitude doesn't necessarily correlate with long-term exercise-induced gains in strength and hypertrophy, making interpretations subject to these limitations.

The Need for Longitudinal Studies

To conclusively settle this debate, longitudinal studies are imperative. A meta-analysis by Schoenfeld and colleagues in December 2013 tackled this issue and concluded that both high- and low-load training can produce significant increases in muscle strength and hypertrophy. However, it favored heavier loads statistically for both outcomes, although with limited statistical power due to the scarcity of studies at that time. Subsequently, more studies have been published on this topic, offering a more robust foundation for practical inferences and subanalyses of potential covariates.

Inclusion Criteria

For trustworthy analysis, we only considered studies published in English-language peer-reviewed journals that met the following criteria: (a) An experimental trial involving both low-load training (≤60% 1RM) and high-load training (>60% 1RM). (b) All sets in the training protocols were performed to momentary muscular failure. (c) At least one method of estimating changes in muscle mass or dynamic, isometric, or isokinetic strength was used. (d) The training protocol lasted for a minimum of 6 weeks. (e) The study involved participants with no known medical conditions or injuries affecting their training capacity.

Search Strategy

Our systematic literature search adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We exhaustively searched through PubMed/MEDLINE, Cochrane Library, and Scopus, from their inception of indexing to March 2017. Our search syntax included keywords related to muscle hypertrophy, muscle strength, resistance training, training intensity, and more. We also meticulously reviewed the reference lists of the articles retrieved in our search for any additional relevant articles. Additionally, forward citation tracking was conducted using Google Scholar to identify more studies.

Coding of Studies

To maintain consistency and accuracy, two investigators individually coded each study. The coding process encompassed various variables, including authorship, publication year, participant demographics, training intervention details, methods for assessing hypertrophy and strength outcomes, region/muscle of the body measured, pre- and post-intervention data, and reported adverse effects and adherence to the training program. To ensure reliability, a random selection of 30% of the studies was recoded. Agreement on coding was established based on a mean agreement of 90%.

Methodological Quality

Each study's quality was independently assessed by two of the authors using the 11-point Physiotherapy Evidence Database (PEDro) scale, which is a valid measure of the methodological quality of randomized trials. The scale was adapted to our specific context by removing items related to blinding, as it's often impossible to blind participants and investigators in supervised exercise interventions. The qualitative methodology ratings were categorized as "excellent," "good," "moderate," and "poor." Our analysis indicated that the studies included were of good to excellent quality.

Calculation of Effect Size

For each outcome related to hypertrophy, we calculated an effect size (ES) by dividing the pretest-posttest change by the pooled pretest standard deviation (SD). We also calculated the percentage change from pretest to posttest and applied an adjustment for small sample bias. The variance around each ES was calculated using the sample size in each study and the mean ES across all studies.

Statistical Analyses

Our statistical analyses employed a random-effects model using robust variance meta-regression for multilevel data structures, with adjustments for small samples. The study was treated as a clustering variable to account for correlated effects within studies, and observations were weighted by the inverse of the sampling variance. Model parameters were estimated using restricted maximum likelihood.

We performed separate meta-regressions on ESs for 1RM, isometric strength, isokinetic strength, body composition, direct assessments of muscle size, and muscle fiber size via biopsy. Load classification (high or low) was included as a moderator in all regression models. To assess the practical significance of the outcomes, we calculated the equivalent percent change for each meta-regression outcome.

Additionally, sensitivity analyses were conducted to identify highly influential studies that could bias the analysis. This involved removing one study at a time and examining the training load predictor. A study was considered influential if its removal resulted in a significant change in the predictor's significance or a substantial change in the coefficient magnitude.

Results

One Repetition Maximum

For the final analysis, we considered 84 effect sizes from 14 studies. The mean effect size across all studies was 1.50, with a 95% confidence interval ranging from 1.01 to 1.99. This corresponds to a mean percent change of 31.6%. Notably, there was a significant difference in mean effect size between high and low loads, favoring high load, with a difference of -0.37.

Additionally, the study-level analysis revealed that high loads produced a significantly greater effect size compared to low loads. There was no observed interaction between training load and the half of the body trained. Our sensitivity analyses did not identify any highly influential studies.

Isometric Strength

For the analysis of isometric strength, we included 23 effect sizes from 8 studies. The mean effect size across all studies was 0.60, corresponding to a mean percent change of 21.5%. Notably, there was no significant difference in mean effect size between high and low loads.

Conclusion

In summary, our comprehensive analysis of the impact of resistance training load on strength and hypertrophy provides valuable insights into the ongoing debate in the fitness community. While both high-load and low-load training can lead to significant increases in muscle strength and hypertrophy, our analysis suggests that high-load training may offer a slight advantage in terms of effect size and percentage gain, especially when measuring one-repetition maximum (1RM). However, the choice between high-load and low-load training should be informed by individual goals, training experience, and considerations of injury risk and fatigue.

Reference Article

Schoenfeld, Brad J.1; Grgic, Jozo2; Ogborn, Dan3; Krieger, James W.4. Strength and Hypertrophy Adaptations Between Low- vs. High-Load Resistance Training: A Systematic Review and Meta-analysis. Journal of Strength and Conditioning Research 31(12):p 3508-3523, December 2017. | DOI: 10.1519/JSC.0000000000002200

Related

https://healthnewstrend.com/resistance-training-improves-muscle-strength-power-and-function-in-older-adults

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