Chen Jou-An, Shih Chi-Chuan, Lin Pay-Fan, Chen Jin-Jong, Lin Kuan-Chia
National Siluo Agriculturial Industrial High School, Yunlin, Taiwan.
Int J Adolesc Med Health. 2012;24(2):153-9. doi: 10.1515/ijamh.2012.023. Epub 2011 Nov 29.
Health-related physical fitness has decreased with age; this is od immense concern to adolescents. School-based health intervention programs can be classified as either population-wide or high-risk approach. Although the population-wide and risk-based approaches adopt different healthcare angles, they all need to focus resources on risk evaluation. In this paper, we describe an exploratory application of cluster analysis and the tree model to collaborative evaluation of students' health- related physical fitness from a high school sample in Taiwan (n=742). Cluster analysis show that physical fitness can be divided into relatively good, moderate and poor subgroups. There are significant differences in biochemical measurements among these three groups. For the tree model, we used 2004 school-year students as an experimental group and 2005 school-year students as a validation group. The results indicate that if sit-and-reach is shorter than 33 cm, BMI is >25.46 kg/m2, and 1600 m run/walk is >534 s, the predicted probability for the number of metabolic risk factors ≥2 is 100% and the population is 41, both results are the highest. From the risk-based healthcare viewpoint, the cluster analysis can sort out students' physical fitness data in a short time and then narrow down the scope to recognize the subgroups. A classification tree model specifically shows the discrimination paths between the measurements of physical fitness for metabolic risk and would be helpful for self-management or proper healthcare education targeting different groups. Applying both methods to specific adolescents' health issues could provide different angles in planning health promotion projects.
与健康相关的身体素质会随着年龄的增长而下降；这是青少年极为关注的问题。基于学校的健康干预项目可分为面向全体学生或针对高危人群的方法。尽管面向全体学生和基于风险的方法采用了不同的医疗保健视角，但它们都需要将资源集中在风险评估上。在本文中，我们描述了聚类分析和树模型在对台湾一所高中样本（n = 742）学生的健康相关身体素质进行协同评估中的探索性应用。聚类分析表明，身体素质可分为相对良好、中等和较差的亚组。这三组之间的生化指标存在显著差异。对于树模型，我们将2004学年的学生作为实验组，2005学年的学生作为验证组。结果表明，如果坐位体前屈短于33厘米，体重指数（BMI）>25.46千克/平方米，且1600米跑/走超过534秒，那么代谢风险因素数量≥2的预测概率为100%，且人数为41，这两个结果都是最高的。从基于风险的医疗保健角度来看，聚类分析可以在短时间内整理出学生的身体素质数据，然后缩小范围以识别亚组。分类树模型具体展示了代谢风险的身体素质测量之间的判别路径，这将有助于针对不同群体进行自我管理或适当的健康教育。将这两种方法应用于特定青少年的健康问题，可以为规划健康促进项目提供不同的视角。
