Deciphering classification systems for neck pain—Understanding the content of classification systems to enhance physiotherapy management of neck pain

Abstract

BackgroundNeck pain is a prevalent and disabling condition. Conservative management of this condition has shown only moderate effects. A solution to improve treatment effectiveness is to sub‐group patients into a classification system (CS) that allows for more personalised care. However, current stratification methods have only shown short‐term efficacy for pain. Given the limitations of these tools, it is pertinent to understand how these CSs are composed to be able to propose alternative patient management solutions.ObjectiveTo identify and examine the different components of classification systems specific to patients with neck‐related conditions.MethodA systematic literature search was performed on 3 databases (PubMed, Scopus and CINAHL). Only systematic reviews, with or without meta‐analysis, and scoping reviews reporting CS with associated treatment for neck pain were included. Bias evaluation was performed through risk of bias in systematic review tools.ResultsFrom the search strategy, 741 citations were retrieved, and seven studies were included. From these studies, 37 CS with associated treatments were extracted. Mobilisations showed that 64% were constructed using physical findings, 61% of CS were guided by symptom modulation, 25% used results of self‐reported questionnaire, 14% used individual characteristics, 14% incorporated cognitive findings, 8% used neurological findings, 3% used results of medical diagnostic test, and 3% incorporated environmental findings. Fear‐avoidance beliefs was the only cognitive parameter considered among CS.ConclusionThis study shows that existing classification systems for neck pain are limited and lack coverage of all potential drivers of pain and disability. The lack of recognition of psychosocial and pain neuroscience parameters may partly explain the limited effectiveness of these tools.