Huang Terry T-K, Roberts Susan B, Howarth Nancy C, McCrory Megan A
School of Nutrition and Exercise Science, Bastyr University, 14500 Juanita Dr. NE, Kenmore, WA 98028, USA.
Obes Res. 2005 Jul;13(7):1205-17. doi: 10.1038/oby.2005.143.
We present an updated method for identifying physiologically implausible dietary reports by comparing reported energy intake (rEI) with predicted energy requirements (pER), and we examine the impact of excluding these reports.
Adult data from the Continuing Survey of Food Intakes by Individuals 1994 to 1996 were used. pER was calculated from the dietary reference intake equations. Within-subject variations and errors in rEI [coefficient of variation (CV) approximately 23%] over 2 days (d), pER (CV approximately 11%), and measured total energy expenditure (mTEE; doubly labeled water, CV approximately 8.2%) were propagated, where +/-1 SD = CV2(rEI)/d + CV2(pER) + CV2(mTEE) = +/-22%. Thus, a report was identified as implausible if rEI was not within 78% to 122% of pER. Multiple cut-offs between +/-1 and +/-2 SD were tested.
%rEI/pER = 81% in the total sample (n = 6499) and progressively increased to 95% in the +/-1 SD sample (n = 2685). The +/-1 to 1.4 SD samples yielded rEI-weight associations closest to the theoretical relationship (mTEE to weight). Weak or spurious diet-BMI associations were present in the total sample; +/-1 to 1.4 SD samples showed the strongest set of associations and provided the maximum n while maintaining biological plausibility.
Our methodology can be applied to different data sets to evaluate the impact of implausible rEIs on health outcomes. Implausible rEIs reduce the overall validity of a sample, and not excluding them may lead to inappropriate conclusions about potential dietary causes of health outcomes such as obesity.
我们提出一种通过比较报告的能量摄入量(rEI)与预测能量需求(pER)来识别生理上不合理的饮食报告的更新方法,并研究排除这些报告的影响。
使用了1994年至1996年个人食物摄入量持续调查中的成人数据。pER由膳食参考摄入量方程计算得出。对2天内rEI的个体内变异和误差[变异系数(CV)约为23%]、pER(CV约为11%)以及测量的总能量消耗(mTEE;双标水法,CV约为8.2%)进行了传播计算,其中±1标准差 = √[CV²(rEI)/天数 + CV²(pER) + CV²(mTEE)] = ±22%。因此,如果rEI不在pER的78%至122%范围内,则该报告被确定为不合理。测试了±1和±2标准差之间的多个截断值。
总样本(n = 6499)中%rEI/pER = 81%,在±1标准差样本(n = 2685)中逐渐增加到95%。±1至1.4标准差样本产生的rEI加权关联最接近理论关系(mTEE与体重)。总样本中存在弱或虚假的饮食与BMI关联;±1至1.4标准差样本显示出最强的一组关联,并在保持生物学合理性的同时提供了最大样本量。
我们的方法可应用于不同数据集,以评估不合理rEI对健康结果的影响。不合理的rEI会降低样本的整体有效性,不排除它们可能导致关于肥胖等健康结果潜在饮食原因的不恰当结论。