Salley James N, Hoover Adam W, Wilson Michael L, Muth Eric R
J Acad Nutr Diet. 2016 Oct;116(10):1568-1577. doi: 10.1016/j.jand.2016.03.007. Epub 2016 Apr 14.
Current methods of self-monitoring kilocalorie intake outside of laboratory/clinical settings suffer from a systematic underreporting bias. Recent efforts to make kilocalorie information available have improved these methods somewhat, but it may be possible to derive an objective and more accurate measure of kilocalorie intake from bite count.
This study sought to develop and examine the accuracy of an individualized bite-based measure of kilocalorie intake and to compare that measure to participant estimates of kilocalorie intake. It was hypothesized that kilocalorie information would improve human estimates of kilocalorie intake over those with no information, but a bite-based estimate of kilocalorie intake would still outperform human estimates.
PARTICIPANTS/SETTINGS: Two-hundred eighty participants were allowed to eat ad libitum in a cafeteria setting. Their bite count and kilocalorie intake were measured. After completion of the meal, participants estimated how many kilocalories they consumed, some with the aid of a menu containing kilocalorie information and some without. Using a train and test method for predictive model development, participants were randomly divided into one of two groups: one for model development (training group) and one for model validation (test group).
Multiple regression was used to determine whether height, weight, age, sex, and waist-to-hip ratio could predict an individual's mean kilocalories per bite for the training sample. The model was then validated with the test group, and the model-predicted kilocalorie intake was compared with human-estimated kilocalorie intake.
Only age and sex significantly predicted mean kilocalories per bite, but all variables were retained for the test group. The bite-based measure of kilocalorie intake outperformed human estimates with and without kilocalorie information.
Bite count might serve as an easily measured, objective proxy for kilocalorie intake. A tool that can monitor bite count may be a powerful assistant to self-monitoring.
目前在实验室/临床环境之外自我监测千卡摄入量的方法存在系统性的低报偏差。最近为提供千卡信息所做的努力在一定程度上改进了这些方法,但或许可以从进食口数得出一个客观且更准确的千卡摄入量测量方法。
本研究旨在开发并检验一种基于个体进食口数的千卡摄入量测量方法的准确性,并将该测量方法与参与者对千卡摄入量的估计进行比较。研究假设是,与没有信息的情况相比,千卡信息会改善人们对千卡摄入量的估计,但基于进食口数的千卡摄入量估计仍将优于人们的估计。
参与者/环境:280名参与者在自助餐厅环境中随意进食。测量他们的进食口数和千卡摄入量。用餐结束后,参与者估计他们摄入了多少千卡,一些人借助包含千卡信息的菜单进行估计,一些人则没有。采用训练和测试方法进行预测模型开发,参与者被随机分为两组之一:一组用于模型开发(训练组),一组用于模型验证(测试组)。
使用多元回归来确定身高、体重、年龄、性别和腰臀比是否可以预测训练样本中个体每口的平均千卡数。然后用测试组对模型进行验证,并将模型预测的千卡摄入量与人类估计的千卡摄入量进行比较。
只有年龄和性别能显著预测每口的平均千卡数,但所有变量都保留用于测试组。基于进食口数的千卡摄入量测量方法在有和没有千卡信息的情况下都优于人类估计。
进食口数可能作为一种易于测量的、客观的千卡摄入量替代指标。一种能够监测进食口数的工具可能是自我监测的有力助手。