Cluster analysis as an effective tool for identifying physical fitness in students: the basis for an innovative approach to optimizing physical education in the university environment

Objective This study analyzes university students’ physical fitness and, based on the results, applies cluster analysis to identify homogeneous groups with aim to optimize physical education programs at the university. Methods A group of 88 first-year students underwent standardized UNIFITTES 6-60 focusing on strength (long jump from a place, sit-ups in 30 s, bent-arm hang test), endurance (20 m shuttle run test), speed (4 × 10 m shuttle run), flexibility (sit and reach test), and anthropometric measurements to determine BMI and WHR. Cluster analysis was used to identify homogeneous groups based on students’ physical fitness and anthropometric profiles. Results The average BMI reached the value of 23.95, with 12% of students falling into obesity. An increased risk of cardiovascular diseases were identified in 19% (WHR). The distance in standing long jump was 212.3 ± 29.2 cm, the number of sit-ups in 30 s was 228.2 ± 4.3 repetitions, the time in bent-arm hang test was 44.9 ± 30.6 s, the reaching distance in the sit and reach test was 4.2 ± 8.8 cm, the time of the 4 × 10 m shuttle run test was 10.4 ± 0.7 s, the distance covered in the 20 m shuttle run test was 45.4 ± 18.6 runs, and the right and left hand grip strength was 50.8 ± 9.6 kg and 49.1 ± 8.7 kg, respectively. Using cluster analysis and ANOVA, three significantly different performance groups were identified: cluster 0 ≼ cluster 1 ≼ cluster 2. Conclusion These findings indicate that cluster analysis is an effective tool for distinguishing physical fitness levels in students. Identification of their performance profiles allows for the optimization of physical education programs.