TCM Pattern Questionnaire for Lateral Elbow Pain: Development of an Instrument via a Delphi ProcessReportar como inadecuado

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Evidence-Based Complementary and Alternative Medicine - Volume 2016 2016, Article ID 7034759, 12 pages -

Research Article

School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Kowloon Tong, Hong Kong

School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

Received 12 February 2016; Accepted 29 May 2016

Academic Editor: Vincenzo De Feo

Copyright © 2016 Marcus Gadau et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Individualized acupuncture treatment has been practiced for pain therapy. This study used acupuncture treatment for lateral elbow pain LEP as an example to study the diagnostic practice of individualized acupuncture treatment. A provisional version of LEP pattern questionnaire was developed based on a recent systematic review on TCM pattern diagnosis for LEP. A Delphi panel of 33 clinical experts from seven different countries was formed, and the Delphi survey was conducted in Chinese and English language for two rounds. Consensus was achieved from all 26 panelists who responded to the second round on 243 items of the instrument, which included a 72-question-long questionnaire. The mean level of expert consensus on the items of the final questionnaire was 85%. Consensus was found on four TCM patterns that could underlie LEP, namely, the wind-cold-dampness pattern, the qi stagnation and blood stasis pattern, the dual deficiency of qi and blood pattern, and the retained dampness-heat pattern. A list of signs and symptoms indicating one of the four TCM patterns and a list of preferred treatment modalities for each pattern were also generated. Our instrument shows considerable content validity. Further validity and reliability studies are under way.

Autor: Marcus Gadau, Shi-Ping Zhang, Wing-Fai Yeung, Zhao-Xiang Bian, and Ai-Ping Lu



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