Christina Liossi
Internet-delivered attentional bias modification training (iABMT) for the management of chronic musculoskeletal pain: A protocol for a randomised controlled trial
Liossi, Christina; Georgallis, Tsampikos; Zhang, Jin; Hamilton, Fiona; White, Paul; Schoth, Daniel Eric
Authors
Tsampikos Georgallis
Jin Zhang
Fiona Hamilton
Paul White Paul.White@uwe.ac.uk
Professor in Applied Statistics
Daniel Eric Schoth
Abstract
Introduction Chronic musculoskeletal pain is a complex medical condition that can significantly impact quality of life. Patients with chronic pain demonstrate attentional biases towards pain-related information. The therapeutic benefits of modifying attentional biases by implicitly training attention away from pain-related information towards neutral information have been supported in a small number of published studies. Limited research however has explored the efficacy of modifying pain-related biases via the internet. This protocol describes a randomised, double-blind, internet-delivered attentional bias modification intervention, aimed to evaluate the efficacy of the intervention on reducing pain interference. Secondary outcomes are pain intensity, state and trait anxiety, depression, pain-related fear, and sleep impairment. This study will also explore the effects of training intensity on these outcomes, along with participants' perceptions about the therapy. Methods and analysis The study is a double-blind, randomised controlled trial with four arms exploring the efficacy of online attentional bias modification training versus placebo training theorised to offer no specific therapeutic benefit. Participants with chronic musculoskeletal pain will be randomised to one of four groups: (1) 10-session attentional modification group; (2) 10-session placebo training group; (3) 18-session attentional modification group; or (4) 18-session placebo training group. In the attentional modification groups, the probe-classification version of the visual-probe task will be used to implicitly train attention away from threatening information towards neutral information. Following the intervention, participants will complete a short interview exploring their perceptions about the online training. In addition, a subgroup analysis for participants aged 16-24 and 25-60 will be undertaken. Ethics and dissemination This study has been approved by the University of Southampton Research Ethics Committee. Results will be published in peer-reviewed journals, academic conferences, and in lay reports for pain charities and patient support groups. Trial registration number NCT02232100; Pre-results.
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 6, 2020 |
Online Publication Date | Feb 20, 2020 |
Publication Date | Feb 20, 2020 |
Deposit Date | Feb 10, 2020 |
Publicly Available Date | May 14, 2020 |
Journal | BMJ Open |
Electronic ISSN | 2044-6055 |
Publisher | BMJ Publishing Group |
Peer Reviewed | Peer Reviewed |
Volume | 10 |
Issue | 2 |
DOI | https://doi.org/10.1136/bmjopen-2019-030607 |
Public URL | https://uwe-repository.worktribe.com/output/5017721 |
Publisher URL | https://bmjopen.bmj.com/content/10/2/e030607.full |
Files
Internet-delivered attentional bias modification training (iABMT) for the management of chronic musculoskeletal pain: A protocol for a randomised controlled trial
(1.7 Mb)
PDF
Licence
http://creativecommons.org/licenses/by/4.0/
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
You might also like
Changes in attitudes towards telemedicine in acute burn care following the Covid-19 pandemic
(2024)
Journal Article
The inadvertently revealing statistic: A systemic gap in statistical training?
(2024)
Journal Article
Downloadable Citations
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search