How to personalise cognitive-behavioural therapy for chronic primary pain using network analysis: study protocol for...

Area of Science:

• Psychology
• Pain Management
• Clinical Research

Background:

• Cognitive-behavioral therapy (CBT) offers moderate effectiveness for chronic primary pain (CPP).
• Personalization and data-driven approaches may enhance CBT efficacy for CPP by addressing patient heterogeneity.
• Network analysis presents a promising method for personalizing CBT interventions.

Purpose of the Study:

• To describe a protocol for personalizing CBT for CPP using network analysis.
• To assess the feasibility and acceptance of a personalized CBT approach.
• To demonstrate a method for selecting and matching interventions based on individual patient data.

Main Methods:

• A single-case experimental design with multiple baselines and ecological momentary assessment (EMA).
• Estimation of person-specific, process-based networks from EMA data to identify treatment targets.
• Algorithm-driven selection of 1 of 10 CBT interventions based on identified targets, delivered in up to 10 sessions.
• Assessment of individual treatment effects using Tau-U and Hedges' g, alongside conventional pain disability measures.

Main Results:

• Feasibility and acceptance will be demonstrated in a sample of 12 adults with CPP.
• Individual treatment effects will be quantified.
• Pre- and post-therapy networks will be estimated to evaluate treatment impact.

Conclusions:

• This proof-of-concept study provides a methodological framework for personalizing CBT for CPP.
• The findings may guide future research in optimizing CBT effectiveness for chronic pain conditions.
• The study emphasizes the importance of tailored, data-driven interventions in pain management.