Are depression, anxiety, and fear-avoidance predictors of outcomes after digital care for chronic musculoskeletal pain?

Abstract Background: Despite compelling evidence identifying psychological predictive factors in in-person rehabilitation, their validity in remote digital care settings remains unknown. Objective: To assess whether fear-avoidance beliefs, depression, and anxiety predict pain outcomes after a digital care program (DCP) for chronic musculoskeletal pain (CMP). Methods: This ad hoc analysis of a decentralized interventional investigation included patients with CMP who underwent a DCP integrating exercise, education, and behavioral change. Pain outcomes were assessed using 4 measures: last pain score, relative pain change, achievement of postintervention mild pain, and pain response (≥30% change or last pain score ≤3). Predictors included baseline scores of Fear-avoidance Beliefs Questionnaire related to Physical Activity, Patient Health 9-item Questionnaire, and Generalized Anxiety Disorder 7-item scale. Structural equation models evaluated their predictive value on pain outcomes, with or without including potential demographic and clinical confounders. Results: Fear-avoidance beliefs and depression symptoms were consistent predictors across all pain outcomes after confounders adjustment. Worse outcomes were associated with higher baseline fear-avoidance beliefs (eg, last pain score: β = 0.15, SE 0.04, P < 0.001; pain response: odds ratio OR 0.86, 95% confidence interval CI 0.78; 0.96, P = 0.001) and depression levels (eg, last pain score: β = 0.14, SE 0.05, P = 0.007; pain response: OR 0.85, 95% CI 0.74; 0.98, P = 0.008). Anxiety did not significantly affect any pain outcome. Sensitivity analyses showed stronger predictive performance when psychological factors were combined with clinical characteristics. Conclusions: Fear-avoidance beliefs and depression consistently predicted pain outcomes, reinforcing their critical role in digital rehabilitation for CMP. Multimodal, tailored approaches targeting these factors may optimize recovery in remote care.