Personalizing resistance training mitigates neuromuscular and perceived fatigue: the Autoregulation Cluster Training method

Antonio Dello Iacono*, Josh Hillan, Kevin Watson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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To compare predetermined and autoregulated resistance training sessions on velocity loss and perceived fatigue. Twenty-six resistance-trained men completed 3 sessions including the back-squat and bench-press exercises matched for load (75% of 1-repetition maximum), volume (24 repetitions), and total rest (240 s). Sessions were randomly performed as traditional set (TRA), 3 sets of 8 repetitions with 120-second interset rests; cluster interset-rest redistribution (IRR), 6 clusters of 4 repetitions with 48-second between-clusters rests; and autoregulation cluster training (ACT), a personalized combination of clusters, repetitions per cluster, and between-clusters rest regulated on a velocity loss threshold. The comparative effects were evaluated on velocity loss outputs measured with a linear encoder and perceived fatigue responses reported using a single-item scale. IRR and ACT induced less velocity loss than TRA (b = -2.09, P < .001). ACT also mitigated velocity loss more than IRR (b = -2.31, P < .001). The back squat resulted in greater velocity loss compared to the bench press (b = 1.83, P < .001). Perceived fatigue responses mirrored the pattern observed for the velocity loss outputs (IRR and ACT vs TRA: b = -0.64, P < .001; ACT vs IRR: b = -1.05, P < .001; back squat vs bench press: b = 0.46, P = .005). IRR and ACT reduced neuromuscular and perceived fatigue, likely due to their cluster-set structures' embedding frequent windows of interset rest. However, the ACT was overall more effective, presumably given its personalized structure.
Original languageEnglish
Pages (from-to)503-511
Number of pages9
JournalInternational Journal of Sports Physiology and Performance
Issue number5
Early online date14 Mar 2023
Publication statusPublished - 31 May 2023


  • autonomy
  • velocity loss
  • perception
  • velocity-based training
  • load monitoring


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