Colby Sarah, Zhou Wenjun, Sowers Morgan F, Shelnutt Karla, Olfert Melissa D, Morrell Jesse, Koenings Mallory, Kidd Tandalayo, Horacek Tanya M, Greene Geoffrey W, Brown Onikia, White Adrienne A, Hoerr Sharon L, Byrd-Bredbenner Carol, Kattelmann Kendra K
Associate Professor, University of Tennessee, Knoxville, Department of Nutrition, Knoxville, TN;, Email:
Assistant Professor, University of Tennessee, Knoxville, Department of Business Analytics and Statistics, Knoxville, TN.
Am J Health Behav. 2017 Jul 1;41(4):378-389. doi: 10.5993/AJHB.41.4.2.
The study purpose was to identify clusters of weight-related behaviors by sex in a college student populations.
We conducted secondary data analysis from online surveys and physical assessments collected in Project Young Adults Eating and Active for Health (YEAH) with a convenience sample of students on 13 college campuses in the United States. We performed 2-step cluster analysis by sex to identify subgroups with homogeneous characteristics and behaviors. We used 8 derivation variables: healthy eating; eating restraints; external cues; stress; fruit/vegetable intake; calories from fat; calories from sugar-sweetened beverages; and physical activity. Contribution of derivation variables to clusters was analyzed with a MANOVA test.
Data from 1594 students were included. Cluster analysis revealed 2-clusters labeled "Healthful Behavior" and "At-risk" for males and females with an additional "Laid Back" cluster for males. "At-risk" clusters had the highest BMI, waist circumference, elevated health risk, and stress and least healthy dietary intake and physical activity. The "Laid Back" cluster had normal weights and the lowest restrained eating, external cues sensitivity, and stress.
Identified differences in characteristics and attitudes towards weight-related behaviors between males and females can be used to tailor weight management programs.
本研究旨在确定大学生群体中按性别划分的与体重相关行为的聚类情况。
我们对“年轻人为健康而饮食与运动”（YEAH）项目中收集的在线调查和身体评估数据进行了二次分析，该项目以美国13所大学校园的学生为便利样本。我们按性别进行了两步聚类分析，以确定具有同质特征和行为的亚组。我们使用了8个衍生变量：健康饮食；饮食限制；外部线索；压力；水果/蔬菜摄入量；来自脂肪的卡路里；来自含糖饮料的卡路里；以及身体活动。通过多变量方差分析测试分析衍生变量对聚类的贡献。
纳入了1594名学生的数据。聚类分析显示，男性和女性均有两个聚类，分别标记为“健康行为”和“有风险”，男性还有一个额外的“悠闲”聚类。“有风险”聚类的体重指数、腰围、健康风险升高以及压力最高，饮食摄入和身体活动最不健康。“悠闲”聚类体重正常，饮食限制、外部线索敏感性和压力最低。
已确定的男性和女性在与体重相关行为的特征和态度上的差异可用于定制体重管理计划。
