Methods for assessing core muscle co-activation in the lumbar region: a narrative review

Core muscle co-activation is a key neuromuscular mechanism for lumbar spine stability, and its quantitative assessment is important for understanding spinal control, identifying dysfunction, guiding rehabilitation, and informing injury prevention. This narrative review critically appraises current methods for assessing lumbar core muscle co-activation, focusing on their theoretical basis, technical characteristics, interpretive value, limitations, and practical applicability.

Current assessment approaches were narratively synthesized and categorized into surface electromyography, co-contraction index calculations, musculoskeletal modeling, and multimodal approaches integrating electromyographic data with biomechanical simulations. The review examined how these methods capture, quantify, and interpret lumbar core muscle co-activation, and how methodological variability affects validity, reproducibility, comparability, and translation.

Surface electromyography is widely used to assess muscle activation but is affected by signal variability, electrode placement, normalization, crosstalk, and task dependency. Co-contraction index calculations quantify simultaneous muscle activation, yet their validity depends on muscle-pair selection, signal processing, time-window definition, and assumptions regarding agonist and antagonist roles. Musculoskeletal modeling estimates muscle force, spinal loading, and biomechanical consequences of co-activation, but outputs depend on anatomical assumptions, optimization criteria, and validation quality. Multimodal approaches may improve biomechanical interpretation but require technical expertise and standardized integration procedures.

Existing methods provide valuable but heterogeneous insights into lumbar neuromuscular control and spinal stability. Future research should prioritize standardized protocols for electromyography processing, muscle selection, task design, co-contraction index computation, and multimodal model validation to enhance reproducibility, comparability, and clinical translation.