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Validation of the Calgary Symptoms of Stress Inventory (C-SOSI) for Predicting Adherence to a Stress Reduction Technique
Author(s) -
Lauren M. Penwell
Publication year - 2012
Language(s) - English
Resource type - Dissertations/theses
DOI - 10.33915/etd.4909
Subject(s) - stressor , cronbach's alpha , psychology , stress reduction , clinical psychology , stress (linguistics) , affect (linguistics) , cognition , physical therapy , medicine , psychometrics , psychiatry , linguistics , philosophy , communication
Validation of the Calgary Symptoms of Stress Inventory (C-SOSI) for Predicting Adherence to a Stress Reduction Technique Lauren M. Penwell Stress is a ubiquitous aspect of modern life that has serious effects on mental and physical health. Many stress reduction techniques are currently available to help combat these effects but non-adherence to them is a significant barrier to their overall effectiveness. The aims of the current study were to: 1) validate the Calgary Symptoms of Stress Inventory (C-SOSI), an instrument that classifies one‟s stress response profile, using traditional psychometric procedures as well as for predicting responses to a laboratory stress situation, and 2) explore the utility of this instrument for predicting adherence following training in a single stress reduction session using guided breathing. Seventy undergraduates (55 women, 15 men) participated in a laboratory study in which they reported their typical stress responses using the C-SOSI, and their actual cognitive, affective, and cardiovascular responses to two stressful tasks were assessed. Next, all participants were trained in and practiced a diaphragmatic breathing relaxation strategy. Adherence, efficacy, and enjoyment of daily practice of the relaxation strategy were measured during a two-week follow-up phase using a web-based recording system. Although internal consistency reliabilities were generally acceptable for the C-SOSI subscales (Cronbach alphas ranging from .78 to .94), the validity of the C-SOSI subscales for explaining variance in actual stress responses to laboratory stressors was not as expected. The C-SOSI Affect subscale explained a significant amount of variance in the cognitive response to stress in the laboratory (R = .21, p < .01), and the C-SOSI Physiology subscale explained a significant amount of variance in both the affective and physiological responses to stress in the laboratory (R = .28, p < .01, and R = .22, p < .01, respectively). None of the C-SOSI subscales explained adherence to daily practice, efficacy, or enjoyment of the breathing relaxation strategy. Participants with both greater active coping and aggregated physiological responses to laboratory stress rated their practice of the relaxation strategy as being more enjoyable than participants with lower responses in both of these domains. In summary, although the C-SOSI Physiology subscale was shown to be associated with actual physiological and affective responses to laboratory stressors, the other subscales of the C-SOSI fared less well, and no C-SOSI subscale was associated with any measure of adherence to daily practice, efficacy, or enjoyment of the breathing relaxation strategy used in this study. Because this study was the first to attempt to demonstrate the validity of the C-SOSI, the instrument requires further attention in future empirical work on measuring the stress responses of healthy adults as well as individuals suffering with a range of medical diseases. PR EV IE W Stress Responses and Stress Reduction iii Acknowledgements I would like to recognize the following individuals for their assistance in completing this project: Heather Elsey, for her help in collecting and entering data; Brian Creasy, for his audio expertise in helping me to make guided breathing recordings; my committee members for their invaluable knowledge and guidance in developing the study; my fellow behavioral physiology labmates for their consultation and support; and the psychology department alumni who so generously contribute to the funding that helped to make this research possible. I would especially like to thank my mentor, Kevin Larkin, for his constant support and guidance over the years. I also would like to acknowledge the Physics Department at the University of Kuopio, Finland, for making their HRV analysis software freely available.

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