Department of Psychiatry and Human Behavior, Weight Control and Diabetes Research Center, The Miriam Hospital/Brown Alpert Medical School, Providence, RI 196 Richmond St., Providence, RI, 02916, USA.
Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, 433 Calhoun Dr, 29634, USA.
Appetite. 2024 Mar 1;194:107176. doi: 10.1016/j.appet.2023.107176. Epub 2023 Dec 27.
Understanding and intervening on eating behavior often necessitates measurement of energy intake (EI); however, commonly utilized and widely accepted methods vary in accuracy and place significant burden on users (e.g., food diaries), or are costly to implement (e.g., doubly labeled water). Thus, researchers have sought to leverage inexpensive and low-burden technologies such as wearable sensors for EI estimation. Paradoxically, one such methodology that estimates EI via smartwatch-based bite counting has demonstrated high accuracy in laboratory and free-living studies, despite only measuring the amount, not the composition, of food consumed. This secondary analysis sought to further explore this phenomenon by evaluating the degree to which EI can be explained by a sensor-based estimate of the amount consumed versus the energy density (ED) of the food consumed. Data were collected from 82 adults in free-living conditions (51.2% female, 31.7% racial and/or ethnic minority; M = 33.5, SD = 14.7) who wore a bite counter device on their wrist and used smartphone app to implement the Remote Food Photography Method (RFPM) to assess EI and ED for two weeks. Bite-based estimates of EI were generated via a previously validated algorithm. At a per-meal level, linear mixed effect models indicated that bite-based EI estimates accounted for 23.4% of the variance in RFPM-measured EI, while ED and presence of a beverage accounted for only 0.2% and 0.1% of the variance, respectively. For full days of intake, bite-based EI estimates and ED accounted for 41.5% and 0.2% of the variance, respectively. These results help to explain the viability of sensor-based EI estimation even in the absence of information about dietary composition.
理解和干预进食行为通常需要测量能量摄入（EI）；然而，常用且广泛接受的方法在准确性上存在差异，并且给用户带来了很大的负担（例如，食物日记），或者实施成本很高（例如，双标记水）。因此，研究人员一直在寻求利用廉价且低负担的技术，例如可穿戴传感器来估计 EI。矛盾的是，一种通过基于智能手表的咬数来估计 EI 的方法，尽管只测量了所消耗食物的数量，而不是其成分，但在实验室和自由生活研究中都表现出了很高的准确性。这项二次分析旨在通过评估基于传感器的消耗量估计值与所消耗食物的能量密度（ED）对 EI 的解释程度，进一步探讨这一现象。从 82 名在自由生活条件下的成年人（51.2%为女性，31.7%为少数族裔/族裔；M=33.5，SD=14.7）中收集数据，他们在手腕上佩戴了一个咬计数器设备，并使用智能手机应用程序实施远程食物摄影法（RFPM）来评估 EI 和 ED 两周。通过一个经过验证的算法生成基于咬的 EI 估计值。在每餐水平上，线性混合效应模型表明，基于咬的 EI 估计值解释了 RFPM 测量的 EI 方差的 23.4%，而 ED 和饮料的存在仅分别解释了方差的 0.2%和 0.1%。对于全天的摄入量，基于咬的 EI 估计值和 ED 分别解释了方差的 41.5%和 0.2%。这些结果有助于解释即使在缺乏有关饮食成分信息的情况下，基于传感器的 EI 估计的可行性。
