Comparison of Data Screening Methods for Evaluating School-Level Fitness Patterns in Youth: Findings from the NFL PLAY 60 FITNESSGRAM Partnership Project

Pedro F. Saint-Maurice^1,2*, Gregory J. Welk¹, Yang Bai¹, Kelly Allums-Featherston³

¹Department of Kinesiology, Iowa State University, Ames, USA.
²School of Psychology-CIPsi, University of Minho, Braga, Portugal.
³NFL PLAY 60 FITNESSGRAM® Project, The Cooper Institute®, Dallas, USA.

DOI: 10.4236/ojpm.2014.411099                

Abstract

Background: There has been a great interest in tracking health-related fitness across the United States. The NFL PLAY 60 FITNESSGRAM Partnership Project (NFL P60FGPP) is a large participatory research network that involves the surveillance of fitness among more than 1000 schools spread throughout the country. Fitness data are collected by school staff and therefore these data can vary in quality and representativeness. Therefore, careful screening procedures are needed to ensure that the data can reflect actual patterns in the schools. This study examined the impact of different data screening procedures on outcomes of aerobic fitness (AF) collected from the NFL P60FGPP. Methods: Data were compiled from 149,101 youth from 504 schools and were processed using the established age- and gender-specific AF FITNESSGRAM health-related standards. Data were subjected to three different screening procedures (based on grade size and boy-to-girl ratio per grade). Linear models were computed to obtain unadjusted and adjusted (for age, BMI-Z, and socio-economic status) estimates of % youth in the Healthy Fitness Zone (HFZ) in order to determine if, 1) there were differences in % in the HFZ and 2) if differences could be explained by changes in the representativeness of the sample due to the different data screening procedures. Results: Depending on the screening procedure used, the final sample ranged from 96,999 (no screening) to 46,572 youth (most stringent criteria). The proportion of youth achieving appropriate levels of AF ranged from 56% to 61% with unscreened data resulting in consistently lower percentages of youth achieving the standard (P < 0.05). Overall, these differences were not explained by possible changes in demographic characteristics as the result of applying different screening criteria. Conclusions: The findings demonstrate the importance of establishing appropriate screening procedures that maximize sample size while also ensuring generalizability of the findings.

Keywords

Aerobic Fitness, Assessment, Data Reduction, Public Health, Surveillance

Conflicts of Interest

The authors declare no conflicts of interest.

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