Patterns (e.g. frequency, amount, etc.) of dietary intake and daily physical activity have each been independently
linked with an increasing prevalence of obesity. Yet, connecting these patterns to obesity and chronic disease
through the integration of time has not been previously considered. Given the strong evidence for independent
linkages between each of these patterns and obesity, there is a critical need to determine the potential synergistic
correlation of these patterns of behavior within the framework of a relationship with health. In the absence of
such insights, opportunities for early detection of behavioral patterns that predispose obesity and chronic disease
will be missed, and our long-term research goal to create these early detection strategies will not be met. The
central hypothesis of this project is based on the analytical framework and methodology that was previously
developed by the investigators: that daily patterns of energy intake, when integrated with physical activity, will
be associated with health in a representative sample of U.S. adults 20 to 65 y (NHANES 2003-2006). The
objectives in this R21 application include the development of data patterning methodology that can be used to
create distinct dietary intake and physical activity patterns and then successfully integrate these patterns to
identify population temporal pattern clusters. Next, the investigators will evaluate the cluster relationships with
obesity and health outcomes and compare the integrated clusters to the un-integrated dietary and activity pattern
clusters. The working hypotheses are firstly, that novel distance measures based on dynamic time warping for
the dietary and physical activity data can be integrated to produce meaningful clustering related to health, and
secondly, that a population cluster which exhibits a pattern of evenly spaced eating occasions, moderate energy
consumption and moderate physical activity patterns in a 24 hour day will be associated with normal weight and
without chronic disease, and that relationships with health outcomes will be stronger for the integrated temporal
pattern clusters compared with un-integrated temporal dietary clusters and physical activity clusters. The
rationale for this research is that its successful completion is expected to create data reduction methods that
classify temporal lifestyle patterns linked to disease, further, the expectation is that these analytical techniques
will integrate multidimensional temporal dietary and physical activity data. These outcomes are expected to have
a significant positive impact, not only in developing/evaluating new analytic methods but in laying the groundwork
for data based preventative interventions. This proposed research is potentially significant because results will
provide a starting point for understanding the importance of the timing of dietary and physical activity patterns to
the prevention of obesity and disease with potentially broad translational
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