Effectiveness of virtual reality in alleviating anxiety and pain during colonoscopy: a protocol for systematic review and meta-analysis of randomised controlled trials
Year: 2025
Type: review
Study design: Study Protocol
Abstract: INTRODUCTION: Colonoscopy is often associated with significant patient pain and anxiety. Virtual reality (VR) technology has been widely used to alleviate pain and anxiety in patients undergoing invasive surgeries. However, there is a lack of reliable evidence supporting its effectiveness in reducing pain and anxiety in patients undergoing colonoscopy. We aim to conduct a meta-analysis to investigate the effectiveness of VR in alleviating pain and anxiety in patients undergoing colonoscopy. METHODS AND ANALYSIS: We will search PubMed, EMBASE, Web of Science and the Cochrane Library from inception to August 2024 for randomised controlled trials evaluating VR interventions for patients undergoing colonoscopy, without language restrictions. Two reviewers will independently screen studies, extract data and assess the risk of bias using the Cochrane Risk of Bias tool. The primary outcomes will be patient-reported pain and anxiety. A meta-analysis will be performed using RevMan V.5.4, with subgroup and meta-regression analyses to explore potential heterogeneity. The certainty of the evidence will be evaluated using the Grading of Recommendations Assessment, Development and Evaluation approach. ETHICS AND DISSEMINATION: This systematic review does not require ethical approval, given that it will not directly utilize information from human participants-instead, the data to be used will be extracted from original studies. Additionally, this systematic review and meta-analysis has been registered with the International Prospective Register of Systematic Reviews (PROSPERO). Following the completion of the systematic review and meta-analysis, we intend to publish the study in an academic journal. PROSPERO REGISTRATION NUMBER: CRD42024580998.
Source: BMJ Open
Authors: Mingqi Zhang, Panpan Zhuang, Weijie Wang, Siyue Fan, Kun Zhang, Yafang Ye, Dandan Kang, Lichun Xu
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,839
OpenAlex ID: W4414055515
