Depression determines illness conviction and pain impact: a structural equation modeling analysis

Area of Science:

• Psychology and Neuroscience
• Pain Medicine
• Psychometric Analysis

Background:

• Headache and orofacial pain conditions are complex, involving behavioral and psychological factors.
• Understanding the interplay of psychological functioning in distinct pain categories is essential for targeted treatment.

Purpose of the Study:

• To develop an algorithm using factor analysis and structural equation modeling (SEM) to describe behavioral and psychological functioning in pain patients.
• To investigate if the factor structure differs across myofascial, neuropathic, and neurovascular pain categories.

Main Methods:

• Utilized the Minnesota Multiphasic Personality Inventory-2 (MMPI-2), Multidimensional Pain Inventory (MPI), Beck Depression Inventory-II (BDI-II), and visual analog scale for functional limitation (VAS-FL).
• Employed Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) via SEM on a split-sample design (N=390).
• Used Analysis of Variance (ANOVA) to compare demographic variables and factor structures across diagnostic groups.

Main Results:

• EFA identified a three-factor solution: Depression, Illness Conviction, and Pain Impact.
• CFA/SEM confirmed this structure and revealed Depression as a critical causal factor influencing Illness Conviction and Pain Impact.
• No significant causal relationship was found between Illness Conviction and Pain Impact; demographic and diagnostic group analyses showed no significant differences.

Conclusions:

• A robust three-factor model (Depression, Illness Conviction, Pain Impact) was validated for headache and orofacial pain patients.
• Depression emerged as a key determinant of illness conviction and pain impact, highlighting it as a primary treatment target.
• The similar psychological factor structure across myofascial, neuropathic, and neurovascular pain suggests shared psychological mechanisms despite differing pathophysiology, underscoring SEM's utility in pain research.