疫情影响下大学生体质健康群体差异分析——基于曲阜师范大学2022级统计学专业学生的体测数据研究

Analysis of Group Differences in Physical Fitness of College Students under the Influence of the Epidemic—Based on the Physical Fitness Test Data of 2022 Statistics Students in Qufu Normal University

摘要: 本研究以曲阜师范大学2022级统计学专业学生为研究对象，采集其身高、体重、肺活量、50米跑等《国家学生体质健康标准》八项核心指标数据，运用描述性统计、系统聚类及K-means聚类等方法分析疫情影响下大学生体质健康的群体差异特征，发现样本群体体质健康水平不均衡，速度类项目与爆发力为普遍薄弱环节，体型均衡者体能表现更优而极端体型存在负向影响，男女生在速度、爆发力和心肺功能方面存在共同短板，体能总分与BMI、肺活量、立定跳远及50米跑等指标具有显著相关性，且通过聚类可将学生分为体能全面均衡的精英体能组、以女生为主且柔韧性优异但力量与耐力不足的柔韧优势组、BMI偏高且速度与柔韧性较差的力量主导组、身材高大但柔韧性与协调性不足且总分最低的特殊形态组(最佳聚类数K = 4)，研究为高校制定个性化体育教学方案及优化运动干预策略提供了数据支撑。

Abstract: This study takes 2022 statistics students at Qufu Normal University as the research subjects, collecting data on eight core indicators from the National Student Physical Fitness Standard, including height, weight, vital capacity, and 50-meter run. Using descriptive statistics, systematic clustering, and K-means clustering, it analyzes the group difference characteristics of college students’ physical fitness under the impact of the epidemic. The study finds that the physical fitness levels of the sample group are unbalanced, with speed-related projects and explosive power being common weak links. Individuals with balanced body types exhibit better physical performance, while extreme body types have a negative impact. Both males and females share shortcomings in speed, explosive power, and cardiopulmonary function. There are significant correlations between total physical fitness scores and indicators such as BMI, vital capacity, standing long jump, and 50-meter run. Through clustering, students can be divided into four groups: The elite physical fitness group (with comprehensive and balanced physical abilities), the flexibility advantageous group (predominantly female, with excellent flexibility but insufficient strength and endurance), the strength-dominated group (with high BMI and poor speed and flexibility), and the special morphology group (tall stature but poor flexibility and coordination, with the lowest total scores), with the optimal number of clusters determined as K = 4. The research provides data support for colleges and universities to develop personalized physical education teaching plans and optimize sports intervention strategies.