Dejan Magoc, Department of Health Studies, Eastern Illinois University, Tanja Magoc, Ph.D., Center for Bioinformatics and Computational Biology, University of Maryland, Joe Tomaka, Ph.D., Department of Public Health, University of Texas at El Paso
Abstract
The risks of developing diabetes, high blood pressure, and cardiovascular disease could
be reduced by increasing the number of individuals receiving adequate levels of physical activity
(PA). Centers for Diseases Control and Prevention (CDC) has reported that about 30% of
Americans do not engage in any PA and about 40% engage in some levels of PA, but still not
meeting the recommended levels defined by the American College of Sports Medicine (ACSM).
Studies have shown that the greatest declines in PA occur during the transitions from high school
to college and beyond. Thus, it is important to identify students at young age that are at health
risk due to lack of PA, so that specific steps could be taken toward helping these individuals
develop a healthier lifestyle. We used data on 100 college students to develop a preliminary
computer program (using a backpropagation multilayer neural network approach) to
automatically identify individuals at risk of being not sufficiently physically active. Besides
various types of demographic variables, data included information on the association between
students self-reported levels of PA and Social Cognitive Theory (SCT) constructs (e.g., selfefficacy,
self-regulation, social support, expectations), as predictors of participation in PA. The
results of this study indicated that the backpropagation multilayer neural network identified and
classified individuals at risk of being not sufficiently physically active into right categories (atrisk
individuals or not at-risk individuals) 77% of the time. Collecting additional data points that
contain more at-risk individuals will improve the neural network’s prediction of at-risk
individuals.
Keywords: automated identification, physical activity, college students
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Submitted 22 April, 2011
Accepted 15 June, 2011