Abstract

Experiencing stress can contribute to unfavorable pain experiences, but outcomes vary across individuals. Evidence suggests that a person's specific reactivity to stressful events may influence pain responses, independent of the type of event. Previous studies measuring physiological markers of stress reactivity such as salivary cortisol and heart rate have found associations with pain both clinically (e.g. recurrent abdominal pain in adolescents, and increased odds of future musculoskeletal pain frequency and severity in young adults) and in the laboratory (i.e. decreased conditioned pain modulation). However, the time and cost required for testing physiological stress reactivity may limit clinical application and reduce external validity. Alternatively, self-reported perception of one's own stress reactivity has been shown to correlate with physiological stress reactivity in relation to health outcomes, and as such may be a valuable tool in predicting pain outcomes. Using data from the Midlife in the United States survey, we selected participants who did not have chronic pain at baseline (n=1512) and who had data at follow-up nine years later. Stress reactivity was assessed using a subscale of the Multidimensional Personality Questionnaire. We conducted a binary logistic regression to determine the odds of developing chronic pain, controlling for demographics and other health related variables (i.e. self-rated physical/mental health, depression, anxiety, BMI, other chronic conditions). Results indicate that higher reported stress reactivity at baseline increased the odds of developing chronic pain at follow-up, OR=1.085, 95% CI [1.021, 1.153], p=0.008, with the only other predictor being number of chronic conditions, OR=1.118, 95% CI [1.045, 1.197], p=.001. Findings provide evidence for the predictive criterion validity of self-reported stress reactivity in the context of chronic pain risk. More generally, with increased need for virtual assessment and care, self-reported stress reactivity may be a useful, time and cost-efficient tool for predicting pain outcomes in research and clinical contexts.

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