From Data to Dialogue: Using Person-Specific Networks to Personalize Cognitive-Behavioral Treatment for Chronic Pain

Objective: Person-specific network models are increasingly proposed for identifying optimal treatment targets, but how they function in clinical decision-making remains largely unknown. This proof-of-concept study integrated ecological momentary assessment (EMA), person-specific networks, and modular evidence-based interventions in the treatment of chronic pain and examined feasibility, preliminary effects, and acceptability.Method: Twelve patients completed treatment with one of nine therapists. An intensive EMA phase (six prompts daily for three weeks) informed network-based module recommendations, which were compared with independent clinical judgment. Subsequent phases were monitored three times weekly and analyzed in a single-case experimental design with randomized baselines. Patients and therapists were interviewed after treatment.Results: Participants provided a mean of 298 observations; seven of 12 met the 80% compliance criterion in all phases. Network-based recommendations largely diverged from clinical judgment. Disability decreased on average (b = -0.45, p = .03), but individual effects were mixed, with no significant baseline-corrected Tau and two participants deteriorating. Patients and therapists primarily valued self-monitoring and structured, data-informed reflection, which increased awareness of everyday patterns and structured the therapeutic dialogue.Conclusions: The clinical value of data-informed personalization may lie less in algorithmic precision than in facilitating patient-centered, reflective clinical reasoning.