Date of Award


Degree Type


Degree Name

Doctor of Philosophy in Psychology


Behavioral Science



First Advisor

Joseph S. Rossi


There is accumulated evidence to support the efficacy of population-based behavioral interventions, however, our understanding of how and why effective interventions promote behavior change is still lacking. The goal of these two studies was to investigate mechanisms of single and multiple behavior change with a focus on cancer risk behaviors, so as to further our understanding of how effective behavioral interventions can promote successful behavior change; improving public health while reducing healthcare costs.

These studies pooled primary data from three large population-based randomized intervention trials that included important cancer-related risk behaviors, including smoking, unhealthy eating, and sun exposure. A total of N=9522 adults across the three samples reported at least one baseline behavioral risk, and were assessed at baseline, 12- and 24-months. Two alternative latent variable modeling techniques were applied to examine behavior change within and jointly across the three cancer risk behaviors.

Latent growth curve (LGC) modeling approaches were employed in the first study to systematically examine 2-year growth trajectories of observed behavioral outcomes within each risk behavior individually and jointly across pairs of co-occurring behavioral risks. Smoking behavior decreased over time across all participants, with treatment predicting a slightly steeper decrease in the number of cigarettes smoked. Conditional LGC models also supported significant intervention effects on increasing healthy eating and sun protection behaviors over time. Parallel- process LGC models revealed that growth trajectories were associated across behaviors within pairs of co-occurring risks.

The second study applied latent transition analysis techniques to examine transitions through the discrete stages for changing individual cancer-related risk behaviors and to compare stage transition patterns across risk behaviors. Stage transition models supported the stability, progression and regression in behavioral stages over time across all three cancer risks. Conditional stage transition models also provided evidence for intervention efficacy for all three behaviors, in terms of moving at-risk participants to reach behavioral criteria, promoting stage progress among those who did not reach criteria, and in maintaining successful behavior change during the follow-up interval. In addition, findings from the second study revealed the stability of precontemplation stage membership across all three behaviors; stage progress from the precontemplation stage was even less likely among control participants.



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