Resistance Training for Hypertrophy in Trained Populations: A Narrative Review and Fatigue-Management Framework (2020–2026)

The prescription of resistance training for muscle hypertrophy in trained populations has shifted from static models requiring failure toward dynamic frameworks centered on fatigue management. This narrative review synthesizes evidence from 2020 to 2026 across five key programming variables, including weekly set volume, proximity to failure, exercise selection and order, range of motion, and autoregulation, to develop an integrated programming framework. A structured search of PubMed, MEDLINE, Scopus, Web of Science, and SPORTDiscus identified published studies from January 2020 to September 2026 involving adults trained with resistance and hypertrophic outcomes. Systematic reviews and pooled analyses were prioritized over individual trials. The synthesis centers on the stimulus to fatigue ratio, defined as mechanical tension versus fatigue generated. Pooled analyses reveal a non-linear relationship between volume and hypertrophy, with 12 to 20 weekly sets per muscle group as a defensible starting range. Failure is unnecessary when volume is adequate; proximity to failure targets of 1 to 3 repetitions in reserve or velocity loss thresholds of 25% to 40% preserve hypertrophic stimulus while limiting disproportionate fatigue. Exercise selection and ordering distribute fatigue; mechanical tension at long muscle lengths drives hypertrophy, with lengthened partials offering regional lower body growth. These variables are integrated into a sequential decision tree algorithm guiding volume titration, exercise selection, effort regulation, and biomechanical optimization. Unlike prior reviews, this work provides a unified framework primarily developed for resistance-trained populations, with potential applicability to clinical and aging populations pending appropriate individual adaptations and further empirical validation. Future research should examine longitudinal periodization and empirically validate the algorithm in applied settings.