A Cluster Analysis and Validation of Health-Related Fitness Tests in College Students

Ryan D. Burns^1, , You Fu², Timothy A. Brusseau¹ and Nora Constantino²

¹Department of Health, Kinesiology, and Recreation, University of Utah, Salt Lake City, USA

²School of Community Health Sciences, University of Nevada Reno, Reno, USA

Cite this paper:
Ryan D. Burns, You Fu, Timothy A. Brusseau and Nora Constantino. A Cluster Analysis and Validation of Health-Related Fitness Tests in College Students. Journal of Physical Activity Research. 2017; 2(2):73-79. doi: 10.12691/jpar-2-2-2

Abstract

Because health-related fitness consists of several domains, understanding clustering of scores from a testing battery can help practitioners derive exercise programs. The purpose of this study was to explore the clustering of health-related fitness test scores in college students and to validate the solution against criterion measures. Participants were college students (Mean age = 19.2 ±0.6 years; N = 523; 342 females, 181 males) recruited from a university in the southwestern U.S. The health-related fitness assessments consisted of BMI, estimated VO_{2 Peak} from the Astrand-Ryhming cycle ergometer test, and a standard push-up test. Criterion measures consisted of DXA-assessed percent body fat (%BF), measured VO_{2 Peak} from a maximal treadmill test, and a 1-Repetition Maximum (1-RM) bench press score. A hierarchical cluster analysis was performed to derive groupings. One-way ANOVA tests were used to explore the differences among the derived cluster groups on each criterion measure. Six cluster groups were formed representing various fitness “phenotypes” (Pseudo-F = 179.7). The cluster groups differed in %BF (F(5, 517) = 44.6, p < 0.001, eta-squared = 0.31), measured VO_{2 Peak} (F(5, 517) = 49.7, p < 0.001, eta-squared = 0.33), and 1-RM bench press scores (F(5, 517) = 17.0, p < 0.001, eta-squared = 0.12), providing validation evidence. Six cluster groups were formed from a health-related fitness test battery in college students that were validated against criterion measures of health-related fitness. The cluster groups can be used to inform current fitness status and for derivation of exercise programs.

Keywords:
exercise fitness health young adult

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