Combining ecological momentary assessment, wrist-based eating detection, and dietary assessment to characterize dietary lapse: A multi-method study protocol.
Combining ecological momentary assessment, wrist-based eating detection, and dietary assessment to characterize dietary lapse: A multi-method study protocol.
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结合生态瞬时评估，基于腕部的饮食检测和饮食评估以表征饮食植物：一种多方法研究方案。
DOI：
10.1177/2055207620988212
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发表时间：
2021-01
期刊：
影响因子：
3.9
通讯作者：
Thomas JG
中科院分区：
文献类型：
--
作者：
Goldstein SP;Hoover A;Evans EW;Thomas JG
行为肥胖治疗(BOT)对许多超重/肥胖者产生了显著的临床减肥和健康益处。然而，BOT中的许多人并没有达到临床上显著的体重减轻和/或体重回升。失误(即饮食偏离BOT规定的饮食)可以解释不良结果，但这种行为没有得到足够的研究，因为它可能很难评估。我们建议使用多种方法来研究失误，这使我们能够识别失误行为的客观测量特征(例如，进食率、持续时间)，检查失误与体重变化之间的关联，并估计失误的营养成分。我们正在招募超重/肥胖的参与者(n = 40)注册一个24周的机器人。参与者每两周完成一次为期7天的生态瞬时评估(EMA)，以自我报告饮食行为，包括饮食失误。参与者持续佩戴戴在手腕上的Actigraph Link来描述进食行为。参与者通过每6周一次的结构化访谈完成24小时的饮食召回，以衡量所有消费的食物和饮料的组成。虽然这项试验的数据收集仍在进行中，但我们提供了三名完成EMA并佩戴运动记录仪的试点参与者的数据，以说明这项工作的可行性、好处和挑战。该方案将是BOT中饮食失误的第一个多方法研究。完成后，这将是已发表的关于被动进食检测和EMA报告的失误的最大研究之一。EMA和被动感知相结合来表征进食，提供了丰富的背景数据，最终将提供对失误行为的细微差别的理解，并使新的干预成为可能。试验注册：注册临床试验NCT03739151；网址：https://clinicaltrials.gov/ct2/show/NCT03739151
Behavioral obesity treatment (BOT) produces clinically significant weight loss and health benefits for many individuals with overweight/obesity. Yet, many individuals in BOT do not achieve clinically significant weight loss and/or experience weight regain. Lapses (i.e., eating that deviates from the BOT prescribed diet) could explain poor outcomes, but the behavior is understudied because it can be difficult to assess. We propose to study lapses using a multi-method approach, which allows us to identify objectively-measured characteristics of lapse behavior (e.g., eating rate, duration), examine the association between lapse and weight change, and estimate nutrition composition of lapse. We are recruiting participants (n = 40) with overweight/obesity to enroll in a 24-week BOT. Participants complete biweekly 7-day ecological momentary assessment (EMA) to self-report on eating behavior, including dietary lapses. Participants continuously wear the wrist-worn ActiGraph Link to characterize eating behavior. Participants complete 24-hour dietary recalls via structured interview at 6-week intervals to measure the composition of all food and beverages consumed. While data collection for this trial is still ongoing, we present data from three pilot participants who completed EMA and wore the ActiGraph to illustrate the feasibility, benefits, and challenges of this work. This protocol will be the first multi-method study of dietary lapses in BOT. Upon completion, this will be one of the largest published studies of passive eating detection and EMA-reported lapse. The integration of EMA and passive sensing to characterize eating provides contextually rich data that will ultimately inform a nuanced understanding of lapse behavior and enable novel interventions. Trial registration: Registered clinical trial NCT03739151; URL: https://clinicaltrials.gov/ct2/show/NCT03739151
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影响因子：
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作者：
Dong, Yujie;Hoover, Adam;Scisco, Jenna;Muth, Eric
通讯作者：
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作者：
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作者：
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通讯作者：
Lissner, L