The Fully Understanding Eating and Lifestyle Behaviors (FUEL) trial: Protocol for a cohort study harnessing digital health tools to phenotype dietary non-adherence behaviors during lifestyle intervention.

IF 2.9 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES DIGITAL HEALTH Pub Date : 2024-08-21 eCollection Date: 2024-01-01 DOI:10.1177/20552076241271783
Stephanie P Goldstein, Kevin M Mwenda, Adam W Hoover, Olivia Shenkle, Richard N Jones, John Graham Thomas
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引用次数: 0

Abstract

Objective: Lifestyle intervention can produce clinically significant weight loss and reduced disease risk/severity for many individuals with overweight/obesity. Dietary lapses, instances of non-adherence to the recommended dietary goal(s) in lifestyle intervention, are associated with less weight loss and higher energy intake. There are distinct "types" of dietary lapse (e.g., eating an off-plan food, eating a larger portion), and behavioral, psychosocial, and contextual mechanisms may differ across dietary lapse types. Some lapse types also appear to impact weight more than others. Elucidating clear lapse types thus has potential for understanding and improving adherence to lifestyle intervention.

Methods: This 18-month observational cohort study will use real-time digital assessment tools within a multi-level factor analysis framework to uncover "lapse phenotypes" and understand their impact on clinical outcomes. Adults with overweight/obesity (n = 150) will participate in a 12-month online lifestyle intervention and 6-month weight loss maintenance period. Participants will complete 14-day lapse phenotyping assessment periods at baseline, 3, 6, 12, and 18 months in which smartphone surveys, wearable devices, and geolocation will assess dietary lapses and relevant phenotyping characteristics. Energy intake (via 24-h dietary recall) and weight will be collected at each assessment period.

Results: This trial is ongoing; data collection began on 31 October 2022 and is scheduled to complete by February 2027.

Conclusion: Results will inform novel precision tools to improve dietary adherence in lifestyle intervention, and support updated theoretical models of adherence behavior. Additionally, these phenotyping methods can likely be leveraged to better understand non-adherence to other health behavior interventions.

Trial registration: This study was prospectively registered https://clinicaltrials.gov/study/NCT05562427.

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充分了解饮食和生活方式行为(FUEL)试验:利用数字健康工具对生活方式干预过程中不坚持饮食的行为进行表型的队列研究方案。
目的:对于许多超重/肥胖症患者来说,生活方式干预能在临床上显著减轻体重,降低疾病风险/严重程度。饮食失误是指在生活方式干预中不遵守推荐饮食目标的情况,与体重减轻和能量摄入增加有关。饮食失误有不同的 "类型"(如吃了计划外的食物、吃得更多),不同饮食失误类型的行为、社会心理和环境机制也可能不同。有些失误类型对体重的影响似乎比其他类型更大。因此,阐明明确的失误类型可能有助于了解和改善生活方式干预的坚持情况:这项为期 18 个月的观察性队列研究将在多层次因素分析框架内使用实时数字评估工具,以发现 "失误表型 "并了解其对临床结果的影响。超重/肥胖成人(n = 150)将参加为期 12 个月的在线生活方式干预和为期 6 个月的减肥维持期。参与者将在基线期、3、6、12 和 18 个月时完成为期 14 天的失误表型评估期,在此期间,智能手机调查、可穿戴设备和地理定位将对饮食失误和相关表型特征进行评估。每个评估阶段都将收集能量摄入量(通过 24 小时饮食回忆)和体重:该试验仍在进行中;数据收集始于 2022 年 10 月 31 日,计划于 2027 年 2 月完成:结论:研究结果将为改善生活方式干预中的饮食依从性提供新的精确工具,并为依从行为的最新理论模型提供支持。此外,这些表型分析方法还可用于更好地了解其他健康行为干预的不依从性:本研究进行了前瞻性注册 https://clinicaltrials.gov/study/NCT05562427。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
DIGITAL HEALTH
DIGITAL HEALTH Multiple-
CiteScore
2.90
自引率
7.70%
发文量
302
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