Department of Human Movement Sciences, Faculty of Health Sciences, University of Fort Hare, Alice, South Africa.
School for Human Movement Sciences, Faculty of Health Sciences, North West University, Potchefstroom, South Africa.
BMC Pediatr. 2020 Nov 17;20(1):527. doi: 10.1186/s12887-020-02419-9.
The aim of this paper was to investigate whether BMI and fat percentage classification criteria, would classify a sample of 7-13 year old boys from a rural background in similar nutritional categories.
A cross-sectional study with a stratified random sampling included 601 rural boys (7-13 years old). Fat percentage criteria classification and BMI were calculated and compared. Maturity status, and age at peak height velocity (PHV) were indirectly determined. Statistical techniques included descriptive statistics, Pearson product correlation coefficients, the Kappa agreement test and the McNemar's test. The level of statistical significance was set at p ≤ 0.05.
All age groups presented with statistically significant high correlations between BMI and fat percentage, and low to medium correlations between fat percentage and maturity age (MA). Measurement of agreement between BMI and fat percentage classifications showed poor to fair agreements for all age groups, with the exception of the eight-year old group which presented a moderate agreement.
Classifications based on BMI and fat percentage, results in different classifications for the same population. Until further research has been done to determine the best classification for nutritional status, it is recommended that both classification methods be used for more accurate classification of nutritional status.
本文旨在研究 BMI 和体脂百分比分类标准是否可以将来自农村背景的 7-13 岁男孩样本分类到相似的营养类别中。
采用横断面研究，采用分层随机抽样，纳入 601 名农村男孩（7-13 岁）。计算并比较体脂百分比标准分类和 BMI。通过间接确定成熟度状态和身高生长速度峰值（PHV）年龄。统计技术包括描述性统计、皮尔逊积差相关系数、Kappa 一致性检验和 McNemar 检验。统计显著性水平设置为 p≤0.05。
所有年龄组的 BMI 和体脂百分比之间均呈高度统计学相关性，体脂百分比和成熟年龄（MA）之间呈低度至中度相关性。BMI 和体脂百分比分类之间的一致性测量显示，除了 8 岁年龄组表现出中度一致性外，所有年龄组的一致性均较差到一般。
基于 BMI 和体脂百分比的分类结果会导致同一人群的不同分类。在进一步研究确定最佳营养状况分类方法之前，建议同时使用两种分类方法以更准确地分类营养状况。
