The effects of fitness self-testing with instant feedback on changes in health-related fitness among Chinese male college students
Abstract
Background The decline in health-related physical fitness resulting from physical inactivity remains a critical global public health concern. Technology-supported fitness self-testing has the potential not only to improve students’ testing experiences but also to enhance their health-related fitness. However, the effectiveness of such approaches has not yet been systematically examined, and their validity within university populations remains largely unestablished. Method A quasi-experimental research design with a control group ( n = 45) and an experimental group ( n = 44), incorporating pre- and post-tests, was employed in this study. The experimental group completed monthly self-testing sessions accompanied by GAI-generated instant feedback over a 16-week period, whereas the control group participated in general physical education classes that included multiple physical activities. Health-related fitness (HRF) was assessed using BMI, the one-mile run, pull-ups, and sit and reach tests. VO₂max was included as a covariate to control for baseline differences in HRF between the two groups. Repeated-measures multivariate analysis of covariance (RM-MANCOVA) was conducted to examine the effects of the intervention on HRF outcomes. Results After controlling for baseline VO 2 max, RM-MANCOVA indicated significant time × group interaction for sit and reach ( p < 0.001) and one-mile run ( p < 0.05), with the intervention group demonstrating significant improvement in both tests. However, no significant differences were observed between groups for body mass index (BMI) and the pull-ups test. Conclusions These findings suggested that HRF self-testing with instant GAI feedback was an effective intervention for improving certain HRF components, particularly flexibility and aerobic fitness. Further research is necessary to explore the long-term effects of self-testing and its application across diverse populations.
Article Details
Authors (10)
Yongshun Wang
Xiaofen D. Hamilton
Rulan Shangguan
Anlu Yang
Na Xiao
Chenhao Wu
Sizhe Liu
Ren Yang
Jiren Zhang
Mark F. Hamilton