# The Clinical Significance of the Manchester Colour Wheel in a Sample of People Treated for Insured Injuries

## TL;DR

This study explores how the Manchester Colour Wheel, combined with machine learning, can assess psychological factors and recovery in people with insured injuries.

## Contribution

The study introduces the use of the Manchester Colour Wheel with machine learning models to predict recovery outcomes in injury patients.

## Key findings

- The MCW showed no significant differences across four injury types in its assessments.
- Machine learning models using MCW data achieved high accuracy in predicting anxiety, depression, and stress.
- Combining MCW with psychometric data improved recovery prediction accuracy, though some models may be overfit.

## Abstract

Background/Objectives: The Manchester Colour Wheel (MCW) was developed as an alternative way of assessing health status, mood and treatment outcomes. There has been a dearth of research on this alternative assessment approach. The present study examines the sensitivity of the MCW to pain, psychological factors and recovery status in 1098 people with insured injuries treated in an interdisciplinary clinic. Methods: A deidentified data set of clients treated in a multidisciplinary clinic was conveyed to the researchers, containing results of MCW and injury-specific psychometric tests at intake, as well as recovery status at discharge. Systematic machine modelling was applied. Results: There were no significant differences between the four injury types studied: motor crash-related Whiplash Associated Disorder (WAD) and workplace-related Shoulder Injury (SI), Back Injury (BI) and Neck Injury (NI) on the MCW. Augmenting the MCW with Machine Learning (ML) models showed overall classification rates for Classification and Regression Tree (CRT) of 75.6% for Anxiety, 70.3% classified for Depression and 68.5% for Stress, and Quick Unbiased Efficient Statistical Trees could identify 68.5% of Pain Catastrophisation and 62.7% of Kinesiophobia. Combining MCW with psychometric measurements markedly increased the predictive power, with a CRT model predicting WAD recovery status with 80.7% accuracy, SI recovery status 81.7% accuracy and BI recovery status with 78% accuracy. A Naïve Bayes Classifier predicted recovery status in NI with 96.4% accuracy. However, this likely represents overfitting. Conclusions: Overall, MCW augmented with ML offers a promising alternative to questionnaires, and the MCW appears to measure some unique psychological features that contribute to recovery from injury.