Date of Award

Summer 1979

Document Type

Dissertation - Restricted

Degree Name

Doctor of Philosophy (PhD)



First Advisor

Tagatz, Glenn E.

Second Advisor

Gawkoski, Roman S.

Third Advisor

Topetzes, Nich J.


The delineation of relationships between the demographic and personality characteristics of chronic pain patients and the success which such patients are able to achieve in relaxation/biofeedback training is a logical area of investigation in light of the growing clinical interest in relaxation/biofeedback training as a mode of treatment for chronic pain patients. Little research has been done in this area. The purpose of the present study was to determine if selected demographic and personality characteristics of chronic pain patients were related to and predictive of success in relaxation/biofeedback training. The subjects were 76 chronic pain patients who completed eight sessions of relaxation/biofeedback training in the Department of Psychology at Curative Workshop. The predictor variables were 6 demographic variables (age, sex, marital status, litigation status, narcotic medication intake, and socioeconomic status) and 16 personality variables (the lJ validity and clinical scale scores of the Minnesota Multiphasic Personality Inventory, the MMPI Low-Back Pain Scale, the MMPI Manifest Anxiety Scale, and the State-Trait Anxiety Inventory scores). The four criterion measures employed included a therapist success rating of the patient as well as three measures based on changes in anxiety, pain ratings, electromyographic biofeedback readings, and activity. To determine the relationships between the predictor and criterion variables, the statistical procedures of Pearson product-moment correlation, principle component analysis, stepwise multiple regression analysis, and double cross-validation were utilized. The results of the 88 correlations between the 22 predictors and the 4 dependent variables showed 8 statistically significant correlations ( p <. 0 5) . Multiple regression analysis produced statistically significant R's (p<.05) and regression equations for each of the predicted variables. Significant relationships between predictor and criterion variables were discussed. Directions for future research in this area were developed.



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