The characteristics and correlative research of “Jin Shang” associated with chronic neck pain in young adults based on ultrasound imaging

Abstract Objective To investigate the characteristics of “Jin Shang”, a specialized term in traditional Chinese medicine (TCM) theory, in young adults with chronic neck pain (CNP) and investigated the correlation of “Jin Shang” with pain intensity and living disabilities using cross-section study. Methods The thickness of the bilateral splenius capitis and semispinalis capitis were measured by ultrasound imaging (USI) as the objective performance of “Jin Shang”. The visual analogue scale (VAS) and Northwick Park Questionnaire (NPQ) were used to assess pain intensity and living disability. The Student's t test was used to investigate the difference in neck extensor muscle (NEM) thickness between CNP patients and healthy controls. Pearson's correlation and multiple linear regression were applied to investigate the relationship between NEM thickness, pain intensity and disability. Results Fifty-nine young adult CNP patients and 16 healthy controls were recruited in this study, in accordance with specific inclusion and exclusion criteria. The student's t test showed that in CNP patients, the thickness of the semispinalis capitis during isometric contraction was significantly thinner than that of healthy controls ( P  = .04). Pearson's correlation analysis also revealed significant relationships between NEM thickness, VAS, and NPQ, while multiple linear regression showed that the thickness of the NEM in CNP patients was a significant predictor of pain intensity and disability. Conclusion There was a significant difference in the thickness of the NEM in young adults with CNP when compared to healthy controls. Alterations in the NEM thickness in both rest and contraction are moderately related to neck pain and living disabilities. Our results investigated the characteristics of “Jin Shang” using USI and revealed a correlation between “Jin Shang” and CNP symptoms, which demonstrates that NEM plays an important role in CNP.