Modeling Errors in Physical Activity Recall Data
Modeling Errors in Physical Activity Recall Data
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DOI：
10.1123/jpah.9.s1.s56
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发表时间：
2012-01-01
影响因子：
3.1
通讯作者：
King, Benjamin M. N.
中科院分区：
文献类型：
--
作者：
Nusser, Sarah M.;Beyler, Nicholas K.;King, Benjamin M. N.
背景资料：身体活动回忆仪器提供了一种收集个体样本的身体活动模式的廉价方法，但它们受到系统和随机测量误差的影响。统计模型可用于估计活动回忆中的测量误差，并为人群提供更准确的日常活动参数估计。研究方法：我们开发了一个测量误差模型的短期活动回忆，回忆和个人的日常活动之间的关系，在很长一段时间。该模型包括系统和随机测量误差的条款。为了估计模型参数，设计应包括重复观察的同时活动回忆和客观监测测量的子样本的受访者。结果：我们用来自爱荷华州体力活动测量研究的初步数据说明了这种方法。在这个数据集中，回忆倾向于高估实际活动，测量误差大大增加了回忆相对于日常活动中人与人之间变化的方差。统计调整用于消除估计通常活动分布时的偏倚和外来变异。结论：回忆数据中的建模测量误差可以用来提供更准确的长期活动行为的估计。
Background: Physical activity recall instruments provide an inexpensive method of collecting physical activity patterns on a sample of individuals, but they are subject to systematic and random measurement error. Statistical models can be used to estimate measurement error in activity recalls and provide more accurate estimates of usual activity parameters for a population. Methods: We develop a measurement error model for a short-term activity recall that describes the relationship between the recall and an individual's usual activity over a long period of time. The model includes terms for systematic and random measurement errors. To estimate model parameters, the design should include replicate observations of a concurrent activity recall and an objective monitor measurement on a subsample of respondents. Results: We illustrate the approach with preliminary data from the Iowa Physical Activity Measurement Study. In this dataset, recalls tend to overestimate actual activity, and measurement errors greatly increase the variance of recalls relative to the person-to-person variation in usual activity. Statistical adjustments are used to remove bias and extraneous variation in estimating the usual activity distribution. Conclusions: Modeling measurement error in recall data can be used to provide more accurate estimates of long-term activity behavior.