Association between incorrect posture and curve types in adolescent idiopathic scoliosis: a large-sample,...

Screening for adolescent idiopathic scoliosis (AIS) has progressed considerably in recent years. However, specific physical indicators for accurately determining spinal curve types are lacking. This study aimed to explore the association between incorrect posture and curve types in AIS.

We analyzed data from 10,453 students who underwent full-spine evaluation in the outpatient department between 2022 and 2023. Assessments included postural observations, Adam's Forward Bending Test, angle of trunk rotation (ATR), and full-spine radiographic imaging. Correlation and logistic regression (LR) models were used to analyze the associations among incorrect posture, curve magnitude, and curve type.

Incorrect postures, particularly the presence of a rib hump, were significantly correlated with the curve magnitude in AIS. Univariate LR analysis showed that female, age, Risser sign, and incorrect postures were identified as associated factors for the curve type. Multivariate LR analysis revealed that right scapular tilt [odds ratio (OR): 1.553, 95% confidence interval (CI): 1.192-2.025, p = 0.001], right rib hump (OR: 1.715, 95% CI: 1.451-2.028, p < 0.001), and right thoracic ATR ≥ 5° (OR: 1.769, 95% CI: 1.484-2.109, p < 0.001) were associated with the thoracic curve; right pelvic tilt (OR: 1.411, 95% CI: 1.253-1.588, p < 0.001) and right rib hump (OR: 1.452, 95% CI: 1.215-1.736, p < 0.001) were associated with the thoracolumbar curve; and left pelvic tilt (OR: 1.266, 95% CI: 1.085-1.477, p = 0.003), right pelvic tilt (OR: 1.321, 95% CI: 1.175-1.485, p = 0.003), and left lumbar eminence (OR: 1.229, 95% CI: 1.033-1.462, p = 0.020) were associated with the lumbar curve.

Incorrect posture and ATR, especially right rib hump, were significantly associated with curve magnitude and curve type in AIS. These indicators can provide an objective reference for triaging children referred from school-based screening for AIS, facilitating a more targeted and efficient identification of at-risk individuals.