A Statistical Framework for Estimating Spinal Compression Risk in Ergonomic Analysis

The NIOSH Lifting Equation is widely used to evaluate manual material handling tasks by identifying lifting risk through load reduction multipliers based on task geometry. However, while it provides a means of classifying risk, it does not estimate spinal forces and therefore cannot quantify the biomechanical load experienced at the lumbar spine. In contrast, biomechanical simulations can estimate spinal compression with high fidelity, but they require motion capture systems and specialized software that are not practical for most workplace assessments. This study aimed to bridge these approaches by developing a family of mixed-effect statistical models that predict L4/L5 spinal compression forces using the geometric parameters of the NIOSH framework combined with posture-specific biomechanical descriptors at peak-loading poses extracted from digital simulations. Data were aggregated from multiple experimental lifting studies in which standardized NIOSH parameters and corresponding spinal compression forces were obtained through validated digital human modeling. Mixed-effects regression was used to establish the relationship between task geometry, joint posture, load weight, and spinal compression. The resulting predictive equation demonstrated strong agreement with simulation-derived forces and effectively captured the contributions of subject, task and body positioning to the spinal compression force across diverse lifting tasks. Importantly, the variance structure of the model’s coefficients allows the contribution of risk to be attributed either to task-related factors or to subject-specific movement behaviors, reinforcing the safety relevance of the framework. Horizontal reach, vertical hand height, and load magnitude emerged as dominant predictors, with trunk posture providing additional explanatory power. The model offers ergonomists a practical, biomechanics-informed tool that extends the descriptive capacity of the NIOSH framework by enabling direct estimation of spinal compression forces without the need for full biomechanical simulations.