Unique Contributions of Acceptance and Catastrophizing on Chronic Pain Adaptation

Julia R. Craner, Jeannie A. Sperry, Afton M. Koball, Eleshia J. Morrison, Wesley P. Gilliam

Pain catastrophizing and acceptance represent distinct but interrelated constructs that influence adaptation to chronic pain. Clinical and laboratory research suggest that higher levels of catastrophizing and lower levels of acceptance predict worse functioning; however, findings have been mixed regarding which specific outcomes are associated with each construct. The current study evaluates these constructs in relation to pain, affect, and functioning in a treatment-seeking clinical sample. Participants included 249 adult patients who were admitted to an interdisciplinary chronic pain rehabilitation program and completed measures of pain and related psychological and physical functioning. Hierarchical multiple regression analyses indicated that pain catastrophizing and acceptance both significantly, but differentially, predicted depressive symptoms and pain-related negative affect. Only pain catastrophizing was a unique predictor of perceived pain severity, whereas acceptance uniquely predicted pain interference and performance in everyday living activities. There were no significant interactions between acceptance and catastrophizing, suggesting no moderation effects. Findings from the current study indicate a pattern of results similar to prior studies in which greater levels of catastrophic thinking is associated with higher perceived pain intensity whereas greater levels of acceptance relate to better functioning in activities despite chronic pain. However, in the current study, both acceptance and catastrophizing were associated with negative affect. These relationships were significant beyond the effects of clinical and demographic variables. These results support the role of pain acceptance as an important contribution to chronic pain-related outcomes alongside the well-established role of pain catastrophizing. Results are limited by reliance on self-report data, cross-sectional design, and low racial/ethnic diversity.