Cross-validation of two independent methods to analyze the sequence of segmental contributions in the cervical spine in extension cineradiographic recordings.

Abstract

BACKGROUND: The sequence of segmental contributions (SSC) offers insight into cervical spine motion, yet accurately analyzing these movements remains challenging. This study compares two tracking methods, developed at two independent centers (AECC and MUMC), to establish their agreement and reliability in measuring SSCs across segments C4 to C7. Understanding spinal biomechanics is crucial for future research into cervical spine pathology and dysfunction. METHODS: Twelve asymptomatic participants (ages 18-35 for "young" and 55-70 for "elderly") performed flexion-extension movements. MUMC + utilized self-directed motion, while AECC used a guided protocol. To ensure comparability, 26 frames from the second half of each extension movement were analyzed. Agreement was assessed using ICCs, Spearman's Rho, and Bland-Altman analysis. Although the sample size is small, a post-hoc power analysis indicated sufficient power, supported by a high volume of analyzed data points. FINDINGS: High intraclass correlation coefficients (ICCs) for the cumulative vertebral rotation (0.97), cumulative intervertebral rotation (0.97) and relative intervertebral rotation (0.93) indicated strong agreement between the two methods. Bland-Altman analysis showed minimal median differences (< 0.2˚) but wider limits of agreement at C6-C7. Normative SSC patterns appeared in 77.8% of younger participants but were absent in elderly participants. INTERPRETATION: This study confirms the reliability of SSC measurement between the two methods, laying the foundation for broader applications. SSC patterns observed in young adults follow a normative pattern, in alignment with previous research. The absence of a fixed pattern in elderly participants could indicate age-related changes or sample variation, warranting cautious interpretation due to the small sample size. Future studies with larger, diverse samples and AI-driven approaches could enhance SSC analysis, enabling better clinical relevance.