Regular physical activity has been shown to be associated with reduced cancer risk (Lee et al, 2006 &McTiernan, 2008). Despite being aware of this, the majority of American adults do not currently meet physical activity guidelines (National Center for Health Statistics, 2007). Our research team has conducted numerous behavioral interventions aimed at encouraging physical activity adoption amongst previously sedentary adults (Marcus et al, 2007;Marcus, Lewis, Williams, Dunsiger et al, 2007;Pekmezi, Neighbors, Lee, Gans, Bock, Morrow, Marquez, Dunsiger &Marcus, 2009). Standard statistical methodology for analyzing data from these longitudinal trials may not suffice to describe patterns of physical activity adoption, maintenance and relapse. We propose a more sophisticated methodology aimed specifically at finding patterns of behavior change in six physical activity interventions. The ability to compare resulting sub-types of behavior change across studies (which themselves overlap in that they contain at least one treatment arm in common) will allow us to determine whether there are consistent patterns across studies and whether the distribution of these sub-types differs by treatment arm. In addition, we will identify potential predictors and moderators of pattern of behavior change by modifying our proposed computing routine to allow for other baseline variables to contribute to the distribution of sub-type. Finally, we will use these sub-types to identify key times in the treatment phase after which relapse to sedentary behavior is more likely. Each of these aims has specific implications for both design and analysis of future physical activity interventions. Using current methods we would be unable to carry out these same analyses and thus might risk missing key information about how participants change their cancer risk.
Although a regular routine of physical activity is known to be associated with a reduced risk of developing cancer, the majority of Americans are not physically active. Understanding the longitudinal patterns of behavior change is key for both the analysis of current data and the design of future interventions. The proposed research has a high significance as it aims to develop sophisticated, multivariate, longitudinal models (and corresponding computing routines) for finding patterns of behavior change across a series of physical activity interventions, which could be disseminated for future use in the analysis of other cancer-control behavioral trials.
|Daiello, Lori A; Gongvatana, Assawin; Dunsiger, Shira et al. (2015) Association of fish oil supplement use with preservation of brain volume and cognitive function. Alzheimers Dement 11:226-35|