Treatment classifications and interventions for neck pain: a scoping review

Iqra Ishaq; Poonam Mehta; Ian W Skinner; Matthew K Bagg; Jasper Bier; Arianne P Verhagen

doi:10.1016/j.jclinepi.2023.04.010

Treatment classifications and interventions for neck pain: a scoping review

J Clin Epidemiol. 2023 Jul:159:1-9. doi: 10.1016/j.jclinepi.2023.04.010. Epub 2023 May 2.

Authors

Iqra Ishaq¹, Poonam Mehta¹, Ian W Skinner², Matthew K Bagg³, Jasper Bier⁴, Arianne P Verhagen⁵

Affiliations

¹ Graduate School of Health, Discipline of Physiotherapy, University of Technology Sydney, Sydney, Australia.
² Graduate School of Health, Discipline of Physiotherapy, University of Technology Sydney, Sydney, Australia; School of Allied Health, Exercise and Sports Sciences, Charles Sturt University, Port Macquarie, Australia.
³ Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, Australia; Perron Institute for Neurological and Translational Science, Perth, Australia; Neuroscience Research Australia, Sydney, Australia.
⁴ Department of General Practice, Erasmus University of Rotterdam, Rotterdam, The Netherlands.
⁵ Graduate School of Health, Discipline of Physiotherapy, University of Technology Sydney, Sydney, Australia. Electronic address: arianne.verhagen@uts.edu.au.

PMID: 37142167
DOI: 10.1016/j.jclinepi.2023.04.010

Abstract

Objectives: Our aim is to provide an overview of how neck pain is classified in the literature, define and group conservative interventions into 'nodes', and develop draft networks of interventions in preparation for a network meta-analysis (NMA).

Study design and settings: We performed a scoping review. For feasibility reasons, we searched for randomized clinical trials (RCTs) via neck pain clinical practice guidelines (CPGs) published from 2014. We used standardized data extraction forms to extract data about classification of neck pain and interventions evaluated in the included RCTs. We calculated frequencies of neck pain classifications and grouped interventions into nodes based on the definitions used in Cochrane reviews. Draft network graphs comparing interventions were constructed using the online Shiny R application CINEMA.

Results: We included 242 RCTs from seven CPGs, evaluating 28,581 patients. We found three different classification systems of which The Neck Pain Task Force classification was used most often. We defined and grouped all interventions into 19 discrete potential nodes.

Conclusion: We found a wide variation in neck pain classifications and conservative interventions. Grouping the interventions was challenging and needs further evaluation before conducting a final NMA.

Keywords: Conservative interventions; Neck pain; Network graphs; Nodes; Scoping review; Transitivity.

Publication types

Meta-Analysis
Review
Research Support, Non-U.S. Gov't

MeSH terms

Humans
Neck Pain* / therapy
Network Meta-Analysis