Department of Kinesiology, Pennsylvania State University, University Park, United States of America.
Department of Kinesiology, Pennsylvania State University, University Park, United States of America.
Obes Res Clin Pract. 2019 Sep-Oct;13(5):430-434. doi: 10.1016/j.orcp.2019.09.003. Epub 2019 Oct 5.
The purpose of this study was to compare obesity classification methods (body mass index (BMI)), abdominal girth (AG), and body fat percentage (BF%)), among college students. College students (n=5943) completed an objective fitness assessment, where height and weight (used to calculate BMI), AG, and BF% (using Bioelectrical Impedance Analysis) were assessed. Correlation and chi-square tests for independence analyses examined relationships between variables and obesity classification methods; and, the sensitivity and specify of BMI using AG and BF% were calculated. Significant correlations were found between BMI and BF% for men (r=0.775, p<0.001) and women (r=0.849, p<0.001); BMI and AG for men (r=0.868, p<0.001) and women (r=0.858, p<0.001); and, BF% and AG for men (r=0.749, p<0.001) and women (r=0.767, p<0.001). There were significant associations between BMI, AG, and BF% for both sexes. Obesity categorization differed significantly between methods. In men and women, respectively, 47.6% and 44.1% classified as normal weight based on BF% were classified as overweight or obese based on BMI (Men: χ2=1547, p<0.001; Women: χ2=1127, p<0.001). In men and women, respectively, 48.3% and 24.0% classified as normal based on AG were classified as overweight or obese using BMI (Men: χ2=1274, p<0.001; Women: χ2=996, p<0.001). Comparing AG and BF%, 25.1% of men and 18.6% of women classified as normal based on AG were classified as overweight or obese using BF% (Men: χ2=1412, p<0.001; Women: χ2=421, p<0.001). Obesity classification differed significantly between methods, and BMI demonstrated relatively poor predictive value with respect to obesity classification. Thus, caution should be applied when using BMI to diagnose obesity among college students.
本研究旨在比较肥胖分类方法（身体质量指数（BMI））、腹围（AG）和体脂百分比（BF%））在大学生中的应用。大学生（n=5943）完成了一项客观的体能评估，其中身高和体重（用于计算 BMI）、AG 和 BF%（使用生物电阻抗分析）进行了评估。相关性和卡方检验用于独立分析变量之间的关系和肥胖分类方法；并计算了 BMI 与 AG 和 BF%的灵敏度和特异性。对于男性（r=0.775，p<0.001）和女性（r=0.849，p<0.001），BMI 与 BF%之间存在显著相关性；对于男性（r=0.868，p<0.001）和女性（r=0.858，p<0.001），BMI 与 AG 之间存在显著相关性；对于男性（r=0.749，p<0.001）和女性（r=0.767，p<0.001），BF%与 AG 之间存在显著相关性。对于男性和女性，BMI、AG 和 BF% 之间存在显著关联。肥胖分类方法之间存在显著差异。分别有 47.6%和 44.1%的男性和女性根据 BF%分类为正常体重的人群根据 BMI 被分类为超重或肥胖（男性： χ2=1547，p<0.001；女性： χ2=1127，p<0.001）。分别有 48.3%和 24.0%的男性和女性根据 AG 分类为正常体重的人群根据 BMI 被分类为超重或肥胖（男性： χ2=1274，p<0.001；女性： χ2=996，p<0.001）。比较 AG 和 BF%，根据 AG 分类为正常体重的男性中有 25.1%和女性中有 18.6%根据 BF%被分类为超重或肥胖（男性： χ2=1412，p<0.001；女性： χ2=421，p<0.001）。肥胖分类方法之间存在显著差异，BMI 对肥胖分类的预测值相对较差。因此，在使用 BMI 诊断大学生肥胖时应谨慎。
