Psychological and sensory predictors of experimental thermal pain: a multifactorial model

Abstract

Although large interindividual differences in pain exist, the underlying factors that contribute to these variations remain poorly understood. Consequently, being able to accurately explain variability in pain ratings in terms of its contributing factors could provide insights into developing a better understanding of individual differences in pain experience. In the present investigation, we show that a significant portion of the variability in experimental heat pain ratings may be predicted using simple quantitative sensory testing and a series of psychological questionnaires including State Trait and Anxiety Inventory (STAI), Center for Epidemiologic Studies - Depression Scale (CES-D), and Positive and Negative Affect Schedule - Expanded form (PANAS-X). A factor analysis was used to reduce individual predictors into sets of composite predictive factors. A multifactorial model that was generated from these factors can reliably predict a significant amount of the variability in heat pain sensitivity ratings (r² = .537, P = .027). Moreover, individual variables including heat pain thresholds and self-assessment of pain sensitivity were found to be poor predictors of heat pain sensitivity. Taken together, these results suggest that a variety of factors underlie individual differences in pain experience and that a reliable model for predicting pain should be constructed from a combination of these factors.

Perspective: The present study provides a way to predict subjects' experimental heat pain sensitivity using a multifactorial model generated from a combination of sensory and psychological factors. Future application of such a model in the studies of clinical pain could potentially improve the quality of care provided for patients in pain.

Figure 1

Multifactorial model of heat pain sensitivity. The multifactorial model of heat pain sensitivity can account for a significant portion of the variability in actual heat pain sensitivity ratings (r²=0.537; p=0.027). The graph shows heat pain sensitivity ratings as predicted by our multifactorial model (regression line) vs. subjects’ actual heat pain sensitivity ratings.