Regression Models for the Erector Spinae Muscle Mass (ESMM) Cross-Sectional Area: Asymptomatic Populations

The objective is to perform morphological analyses and then provide regression models to estimate the ESMM CSA of an individual with his/her subject characteristics. Thirty-five subjects (13 females and 22 males) without low back pain (LBP) history were included in this magnetic resonance imaging (MRI) study. Axial-oblique scans of low back region were used to measure the ESMM CSA. Subject demographics and anthropometrics were obtained and regressed over the ESMM CSA. Best-subset regression analyses were performed. Lean body mass (LBM) and the ankle, wrist, and head indexes were the most frequent predictive variables. Regression models with easy-to-measure variables showed smaller predictive power and increased estimation error compared to other regression models. Practitioners should consider this trade-off between model accuracy and complexity. An individual's ESMM CSA could be estimated by his/her individual characteristics, which enables biomechanical practitioners to estimate individualized low back force capacity and spinal loading.
Source: Journal of Biomechanical Engineering - Category: Biomedical Engineering Source Type: research