Abstract

This study is based on data collected at the Acupuncture clinic of the Local Health Authority (ASL) in Turin from 2008 to 2022 and aims at evaluating the effectiveness of acupuncture in the treatment of musculoskeletal pain using the Numeric Rating Scale (NRS) which analyzes changes in pain perceived by patients in different body regions. The database consists of data provided by patients during the initial visit and the last session. Only patients who provided data at the beginning and end of treatment were included. The data were processed using JASP 0.17.2.1 software. The sample consisted of 932 patients with musculoskeletal conditions, excluding 254 subjects with internal medical conditions, who were treated during the same period. The selected population includes individuals aged 23-94, comprising 242 men and 690 women. Patients followed a therapeutic protocol based on the initial diagnosis and underwent an initial cycle of six weekly sessions, with the possibility of four additional sessions if needed. Acupuncture was performed by experienced medical personnel following Traditional Chinese Medicine guideline. The average NRS values were 7.49 at the beginning and 4.27 at the end, with a 43% reduction in pain. The data were analyzed using the Wilcoxon test, confirming statistical significance (p < 0.001). They were then divided by body region, showing a reduction in pain ranging from 40% to 55%. Statistical analysis among different conditions was performed using the Kruskal-Wallis test, with further comparisons using the Dunn test. The study demonstrates that acupuncture is effective in reducing musculoskeletal suffering, with a significant decrease in pain perceived by patients. The results suggest that acupuncture can be a valid treatment for a wide range of conditions, with pain reduction ranging from 40% to 55% and greater effectiveness for elbow-related conditions. However, it is important to note that sample size may influence the results, and further research is needed to confirm and expand these findings, especially for less-represented conditions by the sample.

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