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快速思考:快速评估情景式未来思维对超重/肥胖参与者延迟折扣的影响。

Think fast: rapid assessment of the effects of episodic future thinking on delay discounting in overweight/obese participants.

机构信息

Addiction Recovery Research Center, Virginia Tech Carilion Research Institute, 2 Riverside Circle, Roanoke, VA, 24018, USA.

Department of Pediatrics, University at Buffalo, Buffalo, NY, USA.

出版信息

J Behav Med. 2017 Oct;40(5):832-838. doi: 10.1007/s10865-017-9857-8. Epub 2017 May 15.

Abstract

Accumulating laboratory-based evidence indicates that reducing delay discounting (devaluation of delayed outcomes) with the use of episodic future thinking (EFT; mental simulation of future events) improves dietary decision-making and other maladaptive behaviors. Recent work has adapted EFT for use in the natural environment to aid in dietary and weight control by engaging participants in EFT repeatedly throughout the day. These efforts may benefit from minimizing the amount of time required for measurement and implementation of EFT. Using Amazon Mechanical Turk in the present study, we show that EFT effectively reduces delay discounting in overweight/obese participants (N = 131) using the recently developed 5-trial, adjusting-delay discounting task, which can be completed rapidly (25 s) and is therefore ideally suited for ecological momentary assessment. Moreover, measures of delay discounting from this task were strongly correlated with those from the commonly used adjusting-amount task (r = .859). Significant effects of EFT on discounting, however, depended on the number of future events participants generated and imagined. Use of a range of events and future time frames (as is typical in the literature) significantly reduced delay discounting, whereas use of only a single event did not.

摘要

越来越多的实验室研究证据表明,通过使用情景性未来思维(EFT;对未来事件的心理模拟)来减少延迟折扣(对延迟结果的贬值),可以改善饮食决策和其他适应不良行为。最近的工作已经将 EFT 改编为在自然环境中使用,通过在一天中多次让参与者参与 EFT,帮助进行饮食和体重控制。这些努力可能受益于最小化 EFT 的测量和实施所需的时间量。在本研究中,我们使用亚马逊土耳其机器人 (Amazon Mechanical Turk) 表明,EFT 使用最近开发的 5 次试验、调整延迟折扣任务,有效地减少了超重/肥胖参与者 (N=131) 的延迟折扣,该任务可以快速完成 (25 秒),因此非常适合生态瞬时评估。此外,该任务的延迟折扣测量值与常用的调整数量任务的测量值高度相关 (r=0.859)。然而,EFT 对折扣的显著影响取决于参与者生成和想象的未来事件数量。使用一系列事件和未来时间框架(如文献中常见的那样)显著降低了延迟折扣,而仅使用单个事件则没有。

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