Training Effort, Volume & Frequency

Concepts like Training Effort, Volume & Frequency are all intricately tied up together. Understanding their interactions can help us shed some light on how we can use these variables to best achieve our results!
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“Sets to failure” is the only definition of training volume that is cosistently correlated to hypertrophy, despite being a very subjective metric that can be difficult to gauge.

Now, this doesn’t mean that ONLY sets taken to failure matter, but it certainly implies some base level of effort needed to be exerted to get at least somewhat close to it.
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Interestingly, the amount of training volume we perform and accumulate weekly, also dictates how often we should go (close or) to failure to maximize results, and vice versa.

This brings us to the inverse relationship of training Volume and Training Effort, which essentially states that:
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With less weekly volume we should train close or to failure the majority of the time, to maximize the stimulus of each training set.
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With more weekly volume instead, we should try to be more conservative and stay further from it, in order to be able to accumulate more sets.
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In a sense, the amount of failure training we perform is inversely proportional to the amount of weekly volume and frequency we choose to train with, where lower training volumes can be seen as “more efficient” ways to train in a way.

However, that does not mean it’s best for everyone. In fact some people may dislike/not know what training to failure means/feels like, and they may want to pick a “softer” approach which can still bring very similar results.

Conversely, others may find no fun in training at sub-maximal effort levels, and may prefer a lower frequency/volume with very hard training.

This is extremely subjective, and many variables do dictate the variability of these recommendations.

However, this model can serve as a great starting point to understand how intricate variables such as effort, volume and frequency are and play together, when writing a program.