CommunicateHealth, Inc, Rockville, MD.
Division of Behavioral Sciences, Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, MI.
J Nutr Educ Behav. 2021 Jan;53(1):28-35. doi: 10.1016/j.jneb.2020.08.002. Epub 2020 Oct 1.
The goal of this study was to explore the impact of 5 decision rules for removing outliers from adolescent food frequency questionnaire (FFQ) data.
This secondary analysis used baseline and 3-month data from a weight loss intervention clinical trial.
African American adolescents (n = 181) were recruited from outpatient clinics and community health fairs.
Data collected included self-reported FFQ and mediators of weight (food addiction, depressive symptoms, and relative reinforcing value of food), caregiver-reported executive functioning, and objectively measured weight status (percentage overweight).
Descriptive statistics examined patterns in study variables at baseline and follow-up. Correlational analyses explored the relationships between FFQ data and key study variables at baseline and follow-up.
Compared with not removing outliers, using decision rules reduced the number of cases and restricted the range of data. The magnitude of baseline FFQ-mediator relationships was attenuated under all decision rules but varied (increasing, decreasing, and reversing direction) at follow-up. Decision rule use increased the magnitude of change in FFQ estimated energy intake and significantly strengthened its relationship with weight change under 2 fixed range decision rules.
Results suggest careful evaluation of outliers and testing and reporting the effects of different outlier decision rules through sensitivity analyses.
本研究旨在探讨 5 种用于消除青少年食物频率问卷(FFQ)数据中异常值的决策规则对数据的影响。
本二次分析使用了一项减肥干预临床试验的基线和 3 个月数据。
从门诊诊所和社区健康集市招募了 181 名非裔美国青少年。
收集的数据包括自我报告的 FFQ 和体重的中介变量(食物成瘾、抑郁症状和食物相对强化值)、照顾者报告的执行功能以及客观测量的体重状况(超重百分比)。
描述性统计分析考察了基线和随访时研究变量的模式。相关性分析探讨了基线和随访时 FFQ 数据与关键研究变量之间的关系。
与不删除异常值相比,使用决策规则减少了病例数并限制了数据范围。在所有决策规则下,基线 FFQ-中介物关系的幅度都减弱了,但在随访时则有所不同(增加、减少和改变方向)。在 2 种固定范围决策规则下,使用决策规则增加了 FFQ 估计能量摄入的变化幅度,并显著加强了其与体重变化之间的关系。
结果表明,需要仔细评估异常值,并通过敏感性分析测试和报告不同异常值决策规则的效果。