Goldstein Stephanie P, Hoover Adam, Evans E Whitney, Thomas J Graham
The Miriam Hospital Weight Control and Diabetes Research Center, Providence, USA.
Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, USA.
Digit Health. 2021 Feb 2;7:2055207620988212. doi: 10.1177/2055207620988212. eCollection 2021 Jan-Dec.
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. Registered clinical trial NCT03739151; URL: https://clinicaltrials.gov/ct2/show/NCT03739151.
行为性肥胖治疗（BOT）能使许多超重/肥胖个体实现具有临床意义的体重减轻并带来健康益处。然而，许多接受BOT治疗的个体并未实现具有临床意义的体重减轻和/或出现体重反弹。失误行为（即饮食偏离BOT规定的饮食）可能解释了不良结果，但这种行为研究较少，因为难以评估。我们建议采用多方法研究失误行为，这使我们能够识别失误行为客观测量的特征（如进食速度、持续时间），研究失误与体重变化之间的关联，并估算失误行为的营养成分。
我们正在招募超重/肥胖参与者（n = 40）参加为期24周的BOT治疗。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。参与者持续佩戴腕式ActiGraph Link来描述饮食行为。参与者每隔6周通过结构化访谈完成24小时饮食回忆，以测量所摄入的所有食物和饮料的成分。
虽然该试验的数据收集仍在进行中，但我们展示了三名完成EMA并佩戴ActiGraph的试点参与者的数据，以说明这项工作的可行性、益处和挑战。
本方案将是第一项对BOT中饮食失误行为进行的多方法研究。完成后，这将是已发表的关于被动进食检测和EMA报告失误行为的最大规模研究之一。将EMA与被动传感相结合来描述饮食行为，可提供丰富的背景数据，最终有助于对失误行为形成细致入微的理解，并促成新的干预措施。注册临床试验NCT03739151；网址：https://clinicaltrials.gov/ct2/show/NCT03739151 。
