Pain Qualities Associated With Body Perception Disturbances: Insights From Machine Learning and SHapley Additive...

To identify specific pain qualities associated with body perception disturbances in patients with musculoskeletal pain, using machine learning interpretability methods.

A cross-sectional study using self-reported questionnaires and SHapley Additive exPlanation (SHAP) analysis within a random forest model.

Multicenter clinical settings where patients with musculoskeletal pain received physical therapy.

A total of 179 patients with musculoskeletal pain, without restrictions on pain location or duration. Patients with cognitive impairment or difficulty completing questionnaires were excluded.

None.

Pain qualities assessed using the Short-Form McGill Pain Questionnaire-2 (SFMPQ-2) and body perception disturbances assessed using the Fremantle Body Awareness Questionnaire (FreBAQ). SHAP values were calculated to quantify the relationship between individual pain qualities and body perception disturbances.

SHAP-based importance ranked "cramping pain" among the top contributors, whereas bivariate correlations with FreBAQ were strongest for "gnawing pain" (r=.90, P<.001), indicating convergence on kinesthesia-related descriptors.

Multiple pain quality descriptors were associated with body perception disturbances. Although the analytic metrics differed in how they ranked individual descriptors, both SHAP-based importance and correlation analyses converged on kinesthesia-related qualities. These results indicate a prominent association between such descriptors and disturbed body perception, potentially informing sensorimotor-focused assessment and intervention strategies.