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Acupuncture vs Noninsertive Sham Acupuncture in Aging Patients With Degenerative Lumbar Spinal Stenosis: A Randomized Controlled Trial

Abstract

Background
Acupuncture is commonly used to treat degenerative lumbar spinal stenosis in Asian countries. However, rigorous data regarding the efficacy and safety of acupuncture for aging patients are currently lacking.

Methods
Eighty patients older than 50 years were assigned randomly to the acupuncture group or the noninsertive sham acupuncture for 24 treatments over an 8-week period. Measurements were obtained at baseline and 4 and 8 weeks of treatment; and 3 and 6 months after completion of treatment. The primary outcome was a change in the 24-point Roland Morris Disability Questionnaire scores at the end of treatment. The secondary outcomes included disability, pain intensity, symptoms, and dysfunction.

Results
At the end of treatment, mean changes in the Roland Morris Disability Questionnaire were -4.1 (95% CI, -4.9 to -3.3) in the acupuncture group and -1.5 (95% CI, -2.3 to -0.7) in the sham group, with a statistically significant between-group difference: -2.6 (95% CI, -3.7 to -1.4). Acupuncture was superior to sham acupuncture in reducing pain intensity (between-group difference: -2.9 [95% CI, -3.8 to -2.0] in leg and buttock pain, vs -2.3 [95% CI, -3.0 to -1.5] in back pain), symptoms and dysfunction (between-group difference: -0.9 [95% CI, -1.2 to -0.6] in symptom subscale, and -0.8 [95% CI, -1.1 to -0.5] in dysfunction subscale).

Conclusions
Acupuncture provides immediate functional recovery and pain relief for degenerative lumbar spinal stenosis. However, current evidence is insufficient to support the suggestion that acupuncture could offer clinical benefits as compared with noninsertive sham acupuncture for degenerative lumbar spinal stenosis.

Published in: Am J Med. 2020 Apr;133(4):500-507.e20. doi: 10.1016/j.amjmed.2019.08.038. Epub 2019 Sep 13.

Quelle: https://www.sciencedirect.com/science/article/pii/S0002934319307612

Autor: Zongshi Qin, Yulong Ding, Chang Xu, Joey S W Kwong, Yuan Ji, Aimin Wu, Jiani Wu, Zhishun Liu

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