ArticleBMC sports science, medicine & rehabilitation2026
Load-velocity relationship during chain-based variable resistance in the smith machine back squat.
Article in BMC sports science, medicine & rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundVelocity-based training (VBT) is well established for prescribing and monitoring intensity in traditional constant resistance training; however, its applicability to chain-based variable resistance training has not been examined. This study examined how different magnitudes of chain-based variable resistance influence commonly used VBT-derived metrics.
methodsThirty-four resistance-trained men (age: 20.50 ± 2.00 years, constant-load one-repetition maximum [1RM]: 1.85 ± 0.22 × body mass) performed Smith machine back squats under three loading configurations (0%, 20%, and 60% of the total external load supported by chains). Across six sessions (two per configuration), participants' mean concentric velocity (MV) was assessed at 45%, 60%, 75%, and 90% of their constant-load 1RM. In three additional sessions (one per configuration), a subset of 21 men (age: 20.29 ± 1.76 years, constant-load 1RM: 1.91 ± 0.19 × body mass) performed four sets to failure at 70% of constant-load 1RM.
resultsThe individual load-MV relationships were highly linear across the three configurations (R
conclusionsAllocating a greater proportion of the load to chains enhances movement velocity at heavy loads and consequently increases the predicted 1RM during the Smith machine back squat. The preserved linearity of the load-MV relationship supports the use of VBT for prescribing and monitoring intensity under variable resistance training; however, the %1RM-MV relationship is configuration-specific and therefore requires tailored calibration according to the proportion of chain load used. Given the growing popularity of combining variable resistance with VBT, the importance of this study lies in providing the first empirical framework for prescribing and monitoring chain-based training intensity using individual %1RM-MV relationships.
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