Targeting individually based psychosocial profiles when treating children with chronic pain and their families is key to effective behavioral health intervention and in line with tenants of precision medicine. Extant research is primarily driven by variable-centered models that focus on broad, group-level differences. The current study adopts a person-centered approach, latent profile analysis (LPA), to identify patient subgroups. Cross-sectional data are presented from 366 children (8–17 years; M = 14.48; standard deviation = 2.36) with chronic pain and a primary caregiver (94% mothers). LPA indicator variables were self-reported: fatigue, internalizing symptoms, pain catastrophizing, and pain acceptance; and parent-reported: pain catastrophizing and responses to child pain. One-way analyses of variances examined the effect of profiles on child age, pain, and function. LPA identified a 4-profile solution. Class 1 (12%) demonstrated the lowest scores (conveying least risk) across 5 of 6 factors. Class 4 (37%) had the highest scores (conveying greatest risk) across all factors. Classes 2 (12%) and 3 (39%) demonstrated more variability across domains. Results revealed significant effects of profile based on child age, pain, and function. This study highlights differential presentation of treatment-modifiable domains within a large sample. LPA methodology is showcased to potentially facilitate clinical conceptualizations and tailored approaches to intervention in pediatric chronic pain.

This article presents a methodological and statistical approach that may be beneficial to better assess individual profiles of pediatric pain functioning. Tools that allow providers to better match patient presentation and intervention are in line with the tenants of precision medicine and may ultimately serve to improve child outcomes.