Profiling physical fitness attributes in college students: A cluster analysis

Peter Hart, Peter Hart
2017 741 International Journal of Physiology   unpublished
Health-related fitness traits vary greatly in general populations, ranging from very low to very high levels. However, less is known regarding fitness trait clustering, Purpose: The purpose of this study was to determine if health-related fitness traits cluster in a college student population. Methods: Data for this research came from a larger fitness measurement study and included N=131 college students attending a rural public university. Ten (10) fitness variables were used in this study.
more » ... first set of five variables represented each component of fitness and were used to construct the latent clusters. The second set of five variables were used to validate the identified clusters. All variables were T-score transformed before analysis. Cluster analysis was performed using the k-means method. Results: Four clusters of individuals were identified in the analysis: 1) Anaerobic and Fit, 2) Aerobic and Fit, 3) Overweight and Unfit, and 4) Normal weight and Unfit. The original set of fitness variables all had significantly different (ps<.001) means across the four cluster groups. The validation tests also showed all five variables with significantly different (ps<.05) means across clusters. Conclusion: Results from this study show that health-related fitness attributes in college students form four specific clusters. These findings may have implications for health promotion marketing of physical activity programs to college students. Introduction The assessment of physical fitness is important because it can provide baseline standards for prescribing a new physical activity program as well as furnish data to gauge those program improvements [1]. There are two main categories of physical fitness: 1) skill-based physical fitness and 2) health-related physical fitness [2]. The later receives more attention because of its associations with health outcomes [3, 4]. There are five defined components of health-related physical fitness [2]. These include cardiorespiratory fitness, muscular strength, muscular endurance, flexibility, and body composition. In college populations, only approximately half of students engage in moderate-intensity physical activity for at least 30 minutes on 5 or more days/week or vigorous-intensity physical activity for at least 20 minutes on 3 or more days/week [5, 6]. Given this, the assessment of health-related physical fitness in college student populations could serve as a viable health promotion intervention strategy in the marketing of physical activity [7]. Successful health promotion marketing strategies also incorporate business techniques such as market segmentation [8]. Therefore, a better understanding of the various subgroups of college students, in terms of health-related fitness, could aid in the application of market segmentation methods. Cluster analysis is a class of statistical techniques that can group participants into homogenous sets based on specified variables [9, 10]. A good cluster analysis outcome would then show participants similar to each other in their assigned cluster and also show that they differ from those in other clusters. There is currently no published empirical evidence showing that fitness traits cluster in college students. The aim of this study was threefold: 1) to determine if fitness traits in college students cluster, 2) to name such clusters if they do exist, and 3) to validate these named clusters by showing how they differ across the same fitness traits from different fitness tests.