Constraining spine stability levels in an optimization model leads to the prediction of trunk muscle cocontraction and improved spine compression force estimates.

A major limitation of optimization models of the spine has been the inability to accurately predict trunk muscle co-activity. Antagonist muscle activity is thought to be necessary to maintain adequate levels of spine stability but, in turn, creates increased loading on the spine. It is thus hypothesized that the CNS attempts to optimize the relationship between spine loading and spine stability in determining muscular activation patterns. This study presents an optimization model of the spine in which stability was constrained to target levels predicted from regression equations of independent loading variables. Objective functions were set to either minimize the sum of the cubed muscle forces or minimize the sum of the squared intervertebral forces at the L4-L5 disc level. Results demonstrate that the inclusion of stability constraints in optimization simulations produced realistic predictions of antagonist muscle activity and predictions of spine compression levels that agree more closely with EMG-based estimates, compared to simulations in which stability was unconstrained. It was concluded that spinal stability is a vital consideration for the CNS when dictating trunk muscle recruitment patterns.