It should be obvious from the name of this site that we’re pretty into science around these parts. When we discuss a particular subject, we try to give a broad, objective overview of all the relevant studies in that area. I’d never claim we’re perfect, but that’s always our aim. However, not everyone is that scrupulous. A common tactic used by many people who aim to appear scientific while still pushing an agenda is called “cherry picking.” Cherry picking refers to discussing only research that supports your point of view, while ignoring or impugning research that disagrees with your biases. In any area of science with a lot of studies being conducted, there are going to be some studies that support one position, and other studies that support the entirely opposite position. The cynic would take that as evidence that science can’t be trusted, but it’s generally much less sinister than that. Simply due to different methodologies, different subject pools, and random chance, you should expect studies to come to differing conclusions. So, how can you avoid cherry picking, but also just avoid saying “some studies say this and some studies say that, so we really have no idea”? Systematic review and meta-analyses.
In a review article, you discuss the findings of many studies instead of primarily just reporting the results of a single study. Not all reviews are created equal, though. In systematic reviews, you follow an extensive set of guidelines to ensure you find and report the results of all of the research in a given area. In non-systematic reviews (sometimes called narrative reviews), you don’t have to report the results of all studies and you have more freedom in how you structure your discussion (i.e. tell a narrative). Some non-systematic reviews are excellent and can be extremely useful because they’re generally a bit more reader-friendly. For example, these are a few very good non-systematic reviews (one, two, three, four). However, non-systematic reviews can also be rife with bias and cherry-picking since they’re not conducted in a systematic way, generally meaning systematic reviews provide a more objective and thorough overview of the literature.
Meta-analyses are simply systematic reviews with the addition of statistical analysis. In a meta-analysis, you pool the results of many studies asking the same (or very similar) research questions to get a quantitative overview of the literature. Maybe 10 studies say A is better than B, 5 say there’s no difference, and 2 say B is better than A. Based on the size of those differences, a meta-analysis may show that, when pooling all results together, A is truly significantly better than B, on average. However, if the 5 studies showing no difference were very large trials, or the two studies in favor of B found very large effects, the meta-analysis may find that there’s no significant difference between A and B, on average, in spite of the majority of studies favoring A.
If you’re familiar with the hierarchy of evidence, systematic reviews and meta-analyses are typically considered the highest quality of evidence. That doesn’t mean they’re perfect – if the literature in a given area is of poor quality, you’re left with a garbage-in-garbage-out scenario – but they’re typically considered to be better and more reliable than individual studies.
Therefore, due to the significance of systematic reviews and meta-analyses, we’ve put together a list and short take-home message of many recent systematic reviews and meta-analyses so you can cut straight to the chase of the results. Many topics related to strength, muscle growth, and nutrition have systematic reviews or meta-analyses covering them. If you’re curious about the research on a given topic, refer back to this list to see if there’s already a systematic review or meta-analysis on the topic. That will give you a better overview than trying to seek out studies one by one (and, if you do want to read the individual studies, it will make your search MUCH easier, since they’ll be referenced in the SR or MA on the topic).
Since there are so many individual systematic reviews or meta-analyses on this list, the overview of each will be really brief. If there are any really major issues, we’ll note them, but for the most part, we’ll just stick to the main findings. Also note that we haven’t included every systematic review or meta-analysis ever done on this list. When there were multiple articles covering the same topic, we went with the one that was more recent or of higher overall quality. If we missed one that you think should be included, let us know in the comments!
To make it easy on you, we split things up by topic. First will be strength, then hypertrophy, then nutrition, then miscellaneous other reviews that are relevant but not neatly categorized.
Just so you’ll know what you’re looking at and reading when viewing the figures below and reading the brief synopses, you’ll need to have an understanding of confidence intervals and forest plots. Confidence intervals (CI) tell you the range of values in which a population average will most likely fall. In meta-analyses, if a confidence interval for comparisons between two different treatments/conditions doesn’t cross zero, then you can state that there’s a statistically significant difference between the two (you have a high level of confidence that the population averages for the two treatments are truly different). Forest plots are figures commonly used in meta-analyses, showing the confidence intervals for multiple studies, along with the pooled average and confidence interval for the entire group of studies.
Here’s an example:
This is a forest plot from a meta-analysis by Schoenfeld et al. looking at the effects of high load vs. low-load training on strength gains. Each black square represents the mean difference in an individual study, while the black bars extending out from that black square represent the confidence interval for that study. The black diamond at the bottom is the confidence interval when pooling the results of all studies. Since the confidence interval doesn’t cross 0, this would be a statistically significant difference, with high-load training leading to significantly larger strength gains than low-load training.
Update, March 2021: The rate at which systematic reviews and meta-analyses are being published has picked up considerably in recent years, and demands on my time have also increased substantially from where they were when this resource went live in 2018. I’m not sure I’ll be able to keep these pages fully updated with summaries and figures for all of the new systematic reviews and meta-analyses moving forward, but I should still have time to provide titles and links for anyone who doesn’t mind doing a tiny bit of legwork. I’ll keep adding the new reviews at the top of each section.
Strength and Physique Systematic Review and Meta-Analysis Master Lists
- Strength: Systematic Review And Meta-Analysis Master List
- Muscle Growth: Systematic Review And Meta-Analysis Master List
- Nutrition And Supplementation: Systematic Review And Meta-Analysis Master List
- Miscellaneous: Systematic Review And Meta-Analysis Master List
- In-House Meta-Analyses: Systematic Review And Meta-Analysis Master List
We’ll update these pages as new systematic review and meta-analyses are published. Feel free to bookmark and refer back to this resource when you want to get a quick overview of a given area of research.
If you enjoy this resource, you’re clearly very passionate about staying up-to-date with the latest research in strength, muscle growth, and body composition. You should check out MASS research review: Monthly Applications in Strength Sport (MASS). Each month, the MASS team reviews the best and most relevant research for strength and physique athletes and coaches, helping you stay on the cutting edge.