Classification Agreement Among Fitnessgram Assessments and Implications for Surveillance Applications

Background/Purpose: The FitnessGram youth fitness assessment battery is the most widely used fitness assessment battery. The adoption of FitnessGram within the Presidential Youth Fitness Program (PYFP) makes FitnessGram the de-facto national fitness battery. Many large districts and states now mandate the use of FitnessGram and the eventual tracking of data within the PYFP may enable reporting of population profiles of health related fitness. Within the FitnessGram battery, schools / teachers have choices of alternative assessments for both aerobic capacity (e.g. PACER and Mile) and Body Composition (BMI and Body Fat). However, the differential use of these items by schools presents challenges for comparing results across schools (and over time) since children may be assessed with different items.

Method: The presentation will summarize issues in the use of alternative assessments in school physical education and the implications for school, district and state level tracking. Methods developed by the Cooper Institute will be shared. Classification agreement between the two body composition assessment (body fat/BMI) and the two aerobic fitness assessments (PACER / Mile) will be shared along with strategies developed by the FitnessGram Scientific Advisory Board to handle these discrepancies for school tracking, research and public health surveillance.   

Analysis/Results: The use of alternative assessments provides teachers and schools with flexibility in using different fitness tests but, due to differences in scoring methods, the assessments can lead to different levels of achievement using the same standardized health related fitness standards. The use of test-equating offers options for standardizing fitness achievement and facilitating longitudinal tracking for school, district and state level surveillance. 

Conclusions: The unique differences in the FitnessGram assessments must be considered when processing and interpreting FitnessGram data for public health surveillance.