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

Background/Objectives: The Manchester Colour Wheel (MCW) was developed as an alternative way of assessing health status, mood and treatment outcomes by Carruthers et al in 2010. There has been a dearth of research on this alternative assessment ap-proach. 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 interdis-ciplinary clinic. Methods: A deidentified data set of clients treated in a multidiscipli-nary clinic were conveyed to the researchers containing results of MCW and injury specific psychometric tests at intake, and recovery status at discharge. Systematic ma-chine modeling was applied. Results: There were no significant differences between the four injury types studied, being 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 Ef-ficient Statistical Trees could identify 68.5% of pain catastrophisation and 62.7% of kinesiophobia. Combining MCW with psychometric measurements markedly in-creased the predictive power with a CRT model predicting WAD recovery status with 80.7% accuracy, SI recovery status 81.7% accuracy, BI recovery status with 78% accu-racy. 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.