Brad Schoenfeld, PhD
@BradSchoenfeld · Jul 28, 2026
Here's a great example of how accumulating evidence refines practical programming recommendations.
2015: I collaborated on a meta-analysis examining training frequency and muscle hypertrophy (https://pubmed.ncbi.nlm.nih.gov/27102172/ Based on the seven studies available at the time, the results suggested that training a muscle twice per week produced greater hypertrophy than training it once per week.
However, with only seven eligible studies, the evidence base was relatively limited.
2019: We revisited the question with an updated meta-analysis (https://t.co/3uTFf4M0pY), this time including 25 studies. The picture changed. When weekly training volume was equated, there was little evidence that training a muscle more than once per week produced greater hypertrophy. However, when volume was not equated, higher frequencies resulted in slightly greater muscle growth, suggesting that distributing training across multiple sessions can help support higher-quality training.
2025: A meta-regression from Mike Zourdos' lab (https://t.co/iVGdA4E6Vl) added another important layer of nuance. Consistent with our 2019 findings, the primary analysis showed that frequency has, at most, a very small independent effect on hypertrophy. However, they also found that per-session volume matters. Once a workout exceeded ~11 direct sets for a muscle, splitting that volume across multiple weekly sessions was predicted to enhance hypertrophy.
The practical takeaway:
≤10 direct sets per muscle per session: Training frequency appears to have little independent effect on muscle growth.
>10–11 direct sets per muscle per session: Increasing frequency becomes advantageous because it distributes volume across the week, potentially improving the quality and effectiveness of the work.
The broader lesson is perhaps even more important than the programming recommendation:
Science is a process, not a collection of fixed truths. As more high-quality evidence accumulates, our understanding improves—and our recommendations should change accordingly. Strong opinions should always be proportional to the strength of the evidence. Science evolves and our recommendations should evolve with it. 💪
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