Exercise, Strength Training

December 21, 2014

Light-Load Training: Can It Build Muscle?

It is often stated that heavy loads (>65% 1RM) are required to promote muscular adaptations; light loads are generally considered ineffective for enhancing these outcomes. Recently, this belief has been challenged by several researchers. It has been proposed that as long as training is carried out to muscular failure, light load training will recruit the full spectrum of motor units (and thus muscle fibers), allowing for gains similar to that of using heavy loads.

Last year, I published a review on the topic in the journal Sports Medicine titled, Is there a minimum intensity threshold for resistance training-induced hypertrophic adaptations?. After thoroughly scrutinizing the body of literature, I ultimately concluded: “Current research indicates that low-load exercise can indeed promote increases in muscle growth in untrained subjects, and that these gains may be functionally, metabolically, and/or aesthetically meaningful.”

However, a narrative review is limited to drawing inferences based on a general sense of the research evaluated; it cannot provide quantification of data. A big issue with resistance training studies is that they are very costly and time-consuming to carry out. This invariably leads to small sample sizes where studies lack statistical power to note a significant difference (a so-called a Type II error). I therefore decided to conduct a meta-analysis, where the data from all relevant studies are pooled to maximize statistical power. and thus provide greater clarity on the topic. I teamed up with my colleagues James Krieger, Jacob Wilson, and Ryan Lowery to carry out the analysis.

What We Did
A systematic search of the literature was conducted to identify studies that would potentially be relevant to the meta-analysis. We filtered through the studies and subjected them to rigid inclusion criteria. To meet eligibility, studies had to:
1. Be a randomized controlled trial involving both low (<60% 1RM)- and high-load (>65% 1RM) training
2. Span at least 6 weeks
3. Directly measure dynamic muscle strength and/or hypertrophy
4. Carried out training to momentary muscular failure in both protocols

A total of 13 studies were identified that met inclusion criteria. Three of these studies did not contain adequate data for computation of effect sizes, leaving a total of 10 studies for analysis. Studies were separately coded by two researchers, and we cross-checked our data for consistency. We then randomly chose 3 studies for recoding to ensure there was no “coder drift.” The results of these studies were converted into effect sizes for comparison between conditions.

What We Found:
No significant differences were seen between low- versus high-load training in either strength or hypertrophy, although a trend for greater increases was noted in both conditions.

What These Results Mean
Results of the meta-analysis support the findings of my narrative review on the topic, showing that substantial hypertrophy and even strength can be achieved by training with light loads. Based purely on statistical probability (i.e. the odds that results are due to chance), there was no difference between using heavy and light loads for gaining strength or muscle. However, several things need to be taken into account when drawing evidence-based conclusions.

First, there was a trend for greater results in both strength and hypertrophy. This is a topic that has not been extensively researched, thereby limiting the statistical power of the meta-analysis. The trends noted would suggest that there is actually a difference favoring the heavy load condition, but statistical power was not great enough to sufficiently detect such a difference. Looking beyond basic probability statistics, other analytic measures provide interesting insight into results. Of particular note was the fact that the effect size (a measure of the magnitude of the difference in results) for strength was was markedly higher in the heavy- vs. light-load condition (2.30 versus 1.23, respectively). The 95% confidence interval differential also favored using heavy loads (CI: -0.18–2.32). Moreover, all 9 studies that investigated strength as an outcome favored high-load training, and six of these studies showed a moderate to strong difference in magnitude of effect. In combination, this provides strong evidence that maximal strength gains require heavier loads.

Effect size data for hypertrophy also favored the high- versus low-load conditions (0.82 vs 0.39), although the differential was not nearly as compelling as for strength. Taken in combination with the trend for significance, this suggests a potential advantage for higher-load training when the goal is maximal hypertrophy.

When reconciling findings, the results of our analysis provide compelling evidence that the use of light loads can be effective for increasing muscle size as well as muscle strength. These findings have wide-ranging implications for many populations, particularly the elderly and those with medical conditions that might preclude the use of use of heavier loads (i.e. osteoarthritis, osteoporosis, etc). Alternatively, those seeking to maximize muscular adaptations would require at least some use of heavy loading. Despite an inability to detect significant differences between conditions, the findings indicate a clear advantage for the use of heavier loads to maximize strength gains. There is a suggestion that heavy loads promote greater hypertrophic increases as well, but this inference is not as convincing. With respect to hypertrophy, it can be hypothesized that combining high- and low-loads could optimize fiber-type specific growth across the spectrum of myofiber isoforms. This hypothesis warrants further study.

A primary limitation of the meta-analysis was that all of the studies analyzed were carried out in untrained individuals; no published study to date has evaluated the topic in well-trained individuals. The good news is that I have completed just such a study, where subjects were all experienced lifters. The study is currently in review. I hope to be able to share results and their implications soon. Stay tuned!