Woods Nicolas, Gilliland Jason, McEachern Louise W, O'Connor Colleen, Stranges Saverio, Doherty Sean, Seabrook Jamie A
School of Health Studies, Western University, London, ON N6A 3K7, Canada.
Human Environments Analysis Laboratory, Western University, London, ON N6A 3K7, Canada.
Nutrients. 2025 Jul 30;17(15):2510. doi: 10.3390/nu17152510.
: Accurate dietary assessment is crucial for nutritional epidemiology, but tools like 24 h recalls (24HRs) face challenges with missing or implausible data. The Automated Self-Administered 24 h Dietary Assessment Tool (ASA24) facilitates large-scale data collection, but its lack of interviewer input may lead to implausible dietary recalls (IDRs), affecting data integrity. Multiple imputation (MI) is commonly used to handle missing data, but its effectiveness in high-variability dietary data is uncertain. This study aims to assess MI's accuracy in estimating nutrient intake under varying levels of missing data. : Data from 24HRs completed by 743 adolescents (ages 13-18) in Ontario, Canada, were used. Implausible recalls were excluded based on nutrient thresholds, creating a cleaned reference dataset. Missing data were simulated at 10%, 20%, and 40% deletion rates. MI via chained equations was applied, incorporating demographic and psychosocial variables as predictors. Imputed values were compared to actual values using Spearman's correlation and accuracy within ±10% of true values. : Spearman's rho values between the imputed and actual nutrient intakes were weak (mean ρ ≈ 0.24). Accuracy within ±10% was low for most nutrients (typically < 25%), with no clear trend by missingness level. Diet quality scores showed slightly higher accuracy, but values were still under 30%. : MI performed poorly in estimating individual nutrient intake in this adolescent sample. While MI may preserve sample characteristics, it is unreliable for accurate nutrient estimates and should be used cautiously. Future studies should focus on improving data quality and exploring better imputation methods.
准确的饮食评估对于营养流行病学至关重要，但像24小时回顾法（24HRs）这样的工具面临着数据缺失或不可信的挑战。自动化自填式24小时饮食评估工具（ASA24）有助于大规模数据收集，但其缺乏访谈者的参与可能会导致不可信的饮食回顾（IDRs），影响数据完整性。多重填补（MI）通常用于处理缺失数据，但其在高变异性饮食数据中的有效性尚不确定。本研究旨在评估MI在不同缺失数据水平下估计营养素摄入量的准确性。
使用了加拿大安大略省743名青少年（13 - 18岁）完成的24HRs数据。根据营养素阈值排除不可信的回顾，创建一个清理后的参考数据集。以10%、20%和40%的删除率模拟缺失数据。应用通过链式方程的多重填补，纳入人口统计学和心理社会变量作为预测因子。使用Spearman相关性以及在真实值±10%范围内的准确性将填补值与实际值进行比较。
填补的营养素摄入量与实际摄入量之间的Spearman相关系数较弱（平均ρ≈0.24）。大多数营养素在±10%范围内的准确性较低（通常<25%），且没有因缺失程度而呈现出明显趋势。饮食质量得分显示出略高的准确性，但数值仍低于30%。
在这个青少年样本中，多重填补在估计个体营养素摄入量方面表现不佳。虽然多重填补可能保留样本特征，但在准确估计营养素方面不可靠，应谨慎使用。未来的研究应专注于提高数据质量并探索更好的填补方法。
