The Impact of Digitally Intelligent-Enabled Prescriptive Physical Education on College Students' Physical Fitness and Body Composition: A Randomized Controlled Trial Based on Aerobics Courses

Abstract

Objective: According to students' physical fitness test results and body composition, combined with a dynamically adjusted "digital intelligence-enabled prescription-based teaching" model, this study investigated its effects on college students' physical fitness levels, body composition (body fat percentage, muscle mass), and specialized skill performance. Methods: A randomized controlled trial design was adopted. A total of 307 female undergraduate students enrolled in the 2023 aerobics elective course at Guizhou University of Traditional Chinese Medicine were randomly assigned to the experimental group (n=152) and the control group (n=155) for a 16-week intervention. The experimental group received personalized exercise prescriptions formulated based on individual physical fitness and body composition baseline data, and used digital intelligence platforms to track weekly implementation and dynamically adjust prescriptions. The control group adopted the traditional teaching model. Before and after the intervention, all participants underwent physical fitness tests and body composition analysis using standard physical testing equipment and a bioelectrical impedance body composition analyzer, and their aerobics skills were evaluated. Results: Analysis of covariance (ANCOVA) showed that after controlling for baseline levels, the experimental group performed significantly better than the control group in the post-test of 50-meter run, standing long jump, sit-ups, and 800-meter run (p<0.05). The mean final body fat percentage (FAT%) of the experimental group was lower than that of the control group, and there was a marginally significant difference in skeletal muscle mass between the two groups (p=0.058). The experimental group also showed significant improvements in extracurricular physical activity levels and exercise self-efficacy. Conclusion: Personalized exercise prescriptions formulated based on individual data and dynamically adjusted with the empowerment of digital intelligence tools can effectively improve multiple physical fitness indicators of college students and show a positive trend in optimizing body composition. This model provides practical evidence for the paradigm shift of college physical education courses from standardized teaching to precise and adaptive health promotion.