Wing-Fai Yeung1, Ka-Fai Chung2, Yee-Man Branda Yu3, Lixing Lao3. 1. School of Nursing, Hong Kong Polytechnic University, Hong Kong SAR, China. 2. Department of Psychiatry, University of Hong Kong, Hong Kong SAR, China. 3. School of Chinese Medicine, University of Hong Kong, Hong Kong SAR, China.
Abstract
OBJECTIVE: Few studies have investigated the predictors of the specific and non-specific effects of acupuncture. The aim of this secondary analysis was to determine patient characteristics that may predict a better treatment response to acupuncture for insomnia. METHODS: We pooled the data of three randomised, double-blind, placebo-controlled trials of acupuncture for insomnia to examine sociodemographic variables, clinical characteristics, baseline sleep-wake variables, and treatment expectancy in relation to acupuncture response. Subjects with an improvement in insomnia severity index (ISI) scores of ≥8 points from baseline to 1 week post-treatment were classified as responders. Factors were compared between responders and non-responders, and also by univariate and multivariate logistic regression analysis. RESULTS: A total of 116 subjects who receivedtraditional needle acupuncture were included, of which 37 (31.9%) were classified as responders. Acupuncture responders had a higher educational level (p<0.01) and higher baseline ISI score (p<0.05), compared to non-responders. In the multivariate logistic regression analysis, only the number of years spent in full-time education remained significant as a predictor of treatment response (OR 1.21, 95% CI 1.06 to 1.38, p<0.01). CONCLUSIONS: Consistent with previous studies, our data suggest that the response to acupuncture is difficult to predict. Although the predictive power of educational level is weak overall, our findings provide potentially valuable information that could be built upon in further research (including a larger sample size), and may help to inform patient selection for the treatment of chronic insomnia with acupuncture in the future. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov: #NCT00839592; Results, #NCT00838994; Results, and #NCT01707706; Results. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.
RCT Entities:
OBJECTIVE: Few studies have investigated the predictors of the specific and non-specific effects of acupuncture. The aim of this secondary analysis was to determine patient characteristics that may predict a better treatment response to acupuncture for insomnia. METHODS: We pooled the data of three randomised, double-blind, placebo-controlled trials of acupuncture for insomnia to examine sociodemographic variables, clinical characteristics, baseline sleep-wake variables, and treatment expectancy in relation to acupuncture response. Subjects with an improvement in insomnia severity index (ISI) scores of ≥8 points from baseline to 1 week post-treatment were classified as responders. Factors were compared between responders and non-responders, and also by univariate and multivariate logistic regression analysis. RESULTS: A total of 116 subjects who received traditional needle acupuncture were included, of which 37 (31.9%) were classified as responders. Acupuncture responders had a higher educational level (p<0.01) and higher baseline ISI score (p<0.05), compared to non-responders. In the multivariate logistic regression analysis, only the number of years spent in full-time education remained significant as a predictor of treatment response (OR 1.21, 95% CI 1.06 to 1.38, p<0.01). CONCLUSIONS: Consistent with previous studies, our data suggest that the response to acupuncture is difficult to predict. Although the predictive power of educational level is weak overall, our findings provide potentially valuable information that could be built upon in further research (including a larger sample size), and may help to inform patient selection for the treatment of chronic insomnia with acupuncture in the future. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov: #NCT00839592; Results, #NCT00838994; Results, and #NCT01707706; Results. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.
Entities:
Keywords:
SLEEP MEDICINE; STATISTICS & RESEARCH METHODS
Authors: Jack D Edinger; Simon Beaulieu-Bonneau; Hans Ivers; Bernard Guay; Lynda Bélanger; Bryan Simmons; Charles M Morin Journal: Sleep Date: 2022-01-11 Impact factor: 5.849