The Transtheoretical Model and Exercise Behavior: A Comparison of Five Staging Methods

This project is an examination of one of the first studies that applied the Transtheoretical Model of Behavior Change to the area of exercise. A core concept of the Transtheoretical Model is the temporal dimension represented by the stages of change. A variety of alternative staging methods have been developed. This study compared a continuous measure of stage membership and four discrete algorithms to stage exercise behavior in the context of a worksite program. In Study I, a previously developed continuous measure of stage membership, the (URICA), was adapted to the area of exercise behavior (URICA-E). The structure of the instrument was replicated using Confirmatory Factor Analysis. One, two, three and four factor models were compared, and a correlated four factor model, representing the four stages of Precontemplation, Contemplation, Action and Maintenance, was found to have the best fit. Fit was improved by reducing the number of items. The 16 item version was confirmed in a second sample. A Cluster Analysis was performed using the four standardized scale scores of the 16 item version of the URICA-E. Nine distinct clusters were found and replicated in a cross validation. Profiles were interpreted and found to have a number of similarities when compared to the profiles previously reported in population using the URICA. In Study II, four discrete algorithms were examined both qualitatively and quantitatively. One of the algorithms, the Pproscal, produced distributions most similar to an alternative staging algorithm employed in a representative sample and was also judged to be the best on the basis of being well-defined and easy to answer. In Study III, comparisons were made between the continuous measure, the URICA-E, and the discrete algorithm, the Pproscal. The profiles were compared and a confusion between Maintenance and Precontemplation was noted. This pointed out the critical nature of the wording of the URICA-E questions. The Pproscal did not produce a high level of agreement in classifying stage when compared to the profiles, leading to the conclusion that the continuous measure is different from the discrete algorithm. The URICA-E (31 items, 16 item revised version, and the four scale scores) were compared with the Pproscal using discriminant function analysis. The 31 items produced the highest rates of correct classification. Recommendations include: (1) using the long form of the URICA-E, (however, it requires a population that all acknowledge the presence of the problem behavior); (2) external validation of the profiles produced