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

OBJECTIVE

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

DESIGN

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

SETTING

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

PARTICIPANTS

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.

INTERVENTIONS: None.

MAIN OUTCOME MEASURE(S): 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.

RESULTS

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.

CONCLUSIONS

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.