Combining passive eating monitoring and ecological momentary assessment to characterize dietary lapses from a lifestyle modification intervention.
Combining passive eating monitoring and ecological momentary assessment to characterize dietary lapses from a lifestyle modification intervention.
复制标题
结合被动饮食监测和生态瞬时评估来表征生活方式改变干预中的饮食失误。
DOI：
10.1016/j.appet.2022.106090
复制
发表时间：
2022
期刊：
影响因子：
5.4
通讯作者：
Thomas,JGraham
中科院分区：
文献类型：
--
作者：
Goldstein,StephanieP;Hoover,Adam;Thomas,JGraham
饮食失误（即，不遵守推荐的饮食目标的具体实例）在生活方式改变计划期间促成次优的体重减轻结果。被动饮食监测可以通过客观评估饮食特征来增强失误测量，这些饮食特征可以是失误的标志（例如，吃得更多）。本研究的目的是评估被动推断的饮食特征（即，叮咬、进食持续时间和进食速率），可以区分饮食失误和非失误进食。超重/肥胖的成年人（n = 25）接受了24周的生活方式改变干预。参与者每两周完成一次生态瞬时评估（EMA;重复的智能手机调查），以自我报告饮食失误和非失误饮食事件。参与者戴着一个手腕设备，捕捉连续的手腕运动。先前验证的算法从手腕数据推断进食事件，并计算咬合计数，持续时间和速率（每次咬合的秒数）。混合效应逻辑回归分析显示，咬数、持续时间或进食率对饮食失误的可能性没有简单的影响。适度分析显示，如果晚上进食次数较少（B=-0.16，p <0.05）、时间较短（B=-0.54，p <0.05）或速度较慢（B= 1.27，p <0.001），则晚上进食更有可能是失误。进食特征之间的统计学显著性相互作用（Bs =-0.30至-0.08，ps < .001）揭示了两种不同的模式。吃的东西是1。比平均值小，慢，短，或2。更大、更快、更长的时间与失效概率的增加有关。这项研究是第一个使用客观的饮食监测来表征整个生活方式改变干预的饮食失误。结果表明，传感器的潜力，以确定非遵守仅使用被动感知的饮食特征的模式，从而最大限度地减少需要在未来的研究自我报告。临床试验注册号NCT 03739151。
Dietary lapses (i.e., specific instances of nonadherence to recommended dietary goals) contribute to suboptimal weight loss outcomes during lifestyle modification programs. Passive eating monitoring could enhance lapse measurement via objective assessment of eating characteristics that could be markers for lapse (e.g., more bites consumed). The purpose of this study was to evaluate if passively-inferred eating characteristics (i.e., bites, eating duration, and eating rate), measured via wrist-worn device, could distinguish dietary lapses from non-lapse eating. Adults (n = 25) with overweight/obesity received a 24-week lifestyle modification intervention. Participants completed ecological momentary assessment (EMA; repeated smartphone surveys) biweekly to self-report on dietary lapses and non-lapse eating episodes. Participants wore a wrist device that captured continuous wrist motion. Previously-validated algorithms inferred eating episodes from wrist data, and calculated bite count, duration, and rate (seconds per bite). Mixed effects logistic regressions revealed no simple effects of bite count, duration, or eating rate on the likelihood of dietary lapse. Moderation analyses revealed that eating episodes in the evening were more likely to be lapses if they involved fewer bites (B= −0.16,p< .05), were shorter (B= −0.54,p< .05), or had a slower rate (B= 1.27,p< .001). Statistically significant interactions between eating characteristics (Bs = −0.30 to −0.08,ps < .001) revealed two distinct patterns. Eating episodes that were 1. smaller, slower, and shorter than average, or 2. larger, quicker, and longer than average were associated with increased probability of lapse. This study is the first to use objective eating monitoring to characterize dietary lapses throughout a lifestyle modification intervention. Results demonstrate the potential of sensors to identify non-adherence using only patterns of passively-sensed eating characteristics, thereby minimizing the need for self-report in future studies.Clinical trials registry numberNCT03739151.