You need to train all the way to failure to maximize muscle growth.
Well, that’s what’s currently being said all around the fitness community, so in this
video, we’re going to look at the new study people are claiming shows that failure training
is indeed best training.
This recent meta-analysis has been dredging through the dark trenches of online fitness
debates, where fitness “influencers,” cut out a small section of the study, strip
it of any meaningful context, and then share it to their audience boasting indisputable
vindication of their preconceived biases.
In other words, just another day at the office for your average influencer.
And that small section is this lovely graph that succinctly summarizes the study’s results.
The graph is pretty straightforward.
The top red line is the outcome for strength gains, and the blue line is muscle hypertrophy,
or muscle growth gains.
The higher the line, the more gains, and the further right, the further from training to
failure, or having more reps in reserve.
Based on this modeling, it proposes that you can achieve similar strength gains regardless
of how far you train from failure.
This finding is not too different from previous research, which most agree that the greatest
mediator for strength is, instead, the load lifted, supported by the principle of specificity.
In other words, to lift heavy, you gotta train heavy.
But the more interesting result is definitely muscle hypertrophy.
If we take this graph at face value, this non-linear relationship we see means that
training all the way to failure yields the best result and going even just a few reps
further results in huge drops in gains.
This is a pretty big contrast to previous data.
Sure, some drop from going too far from failure is expected, like 5 or more reps, but the
superiority of training all the way to failure comes as a huge surprise.
So why is there such a big difference here?
Well, first, the researchers were quite adamant that this graph, and their study in general,
is not exactly intended to be used as direct training guidance.
Which certainly will be convincing enough to stop our IG bros from doing.
Through their analysis, they also realized the immense difficulty of interpreting data
on training to failure, making their outcomes, thus the graph, quite far from telling a clear
story.
Let’s take a closer look at these data issues and understand why we should pump the brakes
a bit.
First’s it’s important to note that this study is a preprint, which means it’s not
yet published, nor has it been peer-reviewed, and it’s best not to extrapolate any information
from it before that happens.
Too late.
But the bigger issue at hand, again, is the data.
With this being a meta-analysis, it compiles data from other studies and trials that investigated
proximity to failure.
26 studies in the case of hypertrophy.
These 26 studies can and do have varying methodologies, including using different measurements to
determine proximity to failure.
Of course, this creates a big problem.
The researchers needed all these different datasets to be defined under one continuous
variable, and that was reps in reserve.
That left the researchers to do the next best thing: estimate.
Essentially, they took these other measurements and predictors like percent rep max, between-group
rep differences, velocity loss, and RPE, and then using subjective calculations, they equated
a corresponding estimated reps in reserve.
Now, give credit to the researchers for even taking on the task of converting all the data.
But in the end, we are left with mostly estimated data with unclear accuracy, making it quite
impossible to draw strong conclusions, and certainly impossible to use it to give training
advice.
Another big issue is the definition of failure itself.
There is currently no scientific standard to define failure and even different terms
for it, like volitional, concentric and momentary failure, can cause further confusion.
It’s possible to have two studies come to two separate conclusions solely because their
intervention methods were predicated on different definitions of failure.
The researchers did try to correct some of this ambiguity but couldn’t correct all
of it, which again, raises the issue of accuracy in the data.
Now, if you’re paying pretty close attention, then you’re probably thinking: Wait, wouldn’t
these accuracy issues be a problem for pretty much ANY meta-analysis on training to failure?
And the answer to that is YES, which makes it even weirder how this unpublished, non-peer-reviewed
study caught so much traction in the first place.
But let’s be real, who can resist such an easily digestible and bias-affirming graph
like this one?
When the study does get published and fully reviewed, we can look even further into it,
especially into their estimation process and how they used pre/post-test values to measure
the main effect.
But once more, let’s go back to the lovely graph.
If we were to similarly model the hypertrophy results of a previous meta-analysis that has
been fully published and peer-reviewed, the results would look more like this.
A non-linear relationship showing similar gains between training to failure and training
a few reps shy of failure.
Ultimately, even though we do have a good amount of failure training data, a lot of
work still needs to be done.
And even if we took this study at face value and did the right thing by not looking at
it in a vacuum but alongside previous analyses, then it probably won’t make a huge of an
impact as some might think.
So, if your favorite fitness influencer is heralding this new study as the definitive
proof to exhaust the shit out of your muscles every set, then it might be time to ditch
them and subscribe to a loveable triangular bro instead.
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your failure-loving friends.
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As always, thank you for watching and don’t forget to get your protein!