Suppr超能文献

数字棉花糖测试(DMT)诊断和监测冲动行为的移动健康应用程序:开发和验证研究。

The Digital Marshmallow Test (DMT) Diagnostic and Monitoring Mobile Health App for Impulsive Behavior: Development and Validation Study.

机构信息

Cornell Tech, Cornell University, New York City, NY, United States.

Feinstein Institute for Medical Research, Northwell Health, Great Neck, NY, United States.

出版信息

JMIR Mhealth Uhealth. 2021 Jan 22;9(1):e25018. doi: 10.2196/25018.

Abstract

BACKGROUND

The classic Marshmallow Test, where children were offered a choice between one small but immediate reward (eg, one marshmallow) or a larger reward (eg, two marshmallows) if they waited for a period of time, instigated a wealth of research on the relationships among impulsive responding, self-regulation, and clinical and life outcomes. Impulsivity is a hallmark feature of self-regulation failures that lead to poor health decisions and outcomes, making understanding and treating impulsivity one of the most important constructs to tackle in building a culture of health. Despite a large literature base, impulsivity measurement remains difficult due to the multidimensional nature of the construct and limited methods of assessment in daily life. Mobile devices and the rise of mobile health (mHealth) have changed our ability to assess and intervene with individuals remotely, providing an avenue for ambulatory diagnostic testing and interventions. Longitudinal studies with mobile devices can further help to understand impulsive behaviors and variation in state impulsivity in daily life.

OBJECTIVE

The aim of this study was to develop and validate an impulsivity mHealth diagnostics and monitoring app called Digital Marshmallow Test (DMT) using both the Apple and Android platforms for widespread dissemination to researchers, clinicians, and the general public.

METHODS

The DMT app was developed using Apple's ResearchKit (iOS) and Android's ResearchStack open source frameworks for developing health research study apps. The DMT app consists of three main modules: self-report, ecological momentary assessment, and active behavioral and cognitive tasks. We conducted a study with a 21-day assessment period (N=116 participants) to validate the novel measures of the DMT app.

RESULTS

We used a semantic differential scale to develop self-report trait and momentary state measures of impulsivity as part of the DMT app. We identified three state factors (inefficient, thrill seeking, and intentional) that correlated highly with established measures of impulsivity. We further leveraged momentary semantic differential questions to examine intraindividual variability, the effect of daily life, and the contextual effect of mood on state impulsivity and daily impulsive behaviors. Our results indicated validation of the self-report sematic differential and related results, and of the mobile behavioral tasks, including the Balloon Analogue Risk Task and Go-No-Go task, with relatively low validity of the mobile Delay Discounting task. We discuss the design implications of these results to mHealth research.

CONCLUSIONS

This study demonstrates the potential for assessing different facets of trait and state impulsivity during everyday life and in clinical settings using the DMT mobile app. The DMT app can be further used to enhance our understanding of the individual facets that underlie impulsive behaviors, as well as providing a promising avenue for digital interventions.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03006653; https://www.clinicaltrials.gov/ct2/show/NCT03006653.

摘要

背景

经典的棉花糖测试中,孩子们可以选择一种小的但即时的奖励(例如,一个棉花糖)或一个更大的奖励(例如,两个棉花糖),如果他们等待一段时间的话。这一测试引发了大量关于冲动反应、自我调节以及临床和生活结果之间关系的研究。冲动是自我调节失败的一个显著特征,导致不良的健康决策和结果,因此,理解和治疗冲动是建立健康文化最重要的构建之一。尽管有大量的文献基础,但由于该结构的多维性质和日常生活中评估方法的有限性,冲动性的测量仍然很困难。移动设备和移动健康(mHealth)的兴起改变了我们远程评估和干预个体的能力,为动态诊断测试和干预提供了途径。使用移动设备进行纵向研究可以进一步帮助我们了解冲动行为和日常生活中状态冲动的变化。

目的

本研究旨在开发和验证一个名为数字棉花糖测试(DMT)的基于移动设备的冲动性 mHealth 诊断和监测应用程序,该应用程序同时适用于苹果和安卓平台,以便广泛分发给研究人员、临床医生和普通大众。

方法

DMT 应用程序是使用苹果的 ResearchKit(iOS)和安卓的 ResearchStack 开源框架开发的,用于开发健康研究应用程序。DMT 应用程序由三个主要模块组成:自我报告、生态瞬时评估和主动行为和认知任务。我们进行了一项为期 21 天评估期的研究(N=116 名参与者),以验证 DMT 应用程序的新测量方法。

结果

我们使用语义差异量表来开发 DMT 应用程序中的冲动性特质和瞬时状态的自我报告测量方法。我们确定了三个状态因素(效率低下、寻求刺激和有意),与已建立的冲动性测量方法高度相关。我们进一步利用瞬时语义差异问题来研究个体内变异性、日常生活的影响以及情绪对状态冲动和日常冲动行为的情境影响。我们的结果表明,自我报告的语义差异和相关结果,以及移动行为任务(包括气球模拟风险任务和 Go-No-Go 任务)得到了验证,而移动延迟折扣任务的验证效果相对较低。我们讨论了这些结果对 mHealth 研究的设计意义。

结论

这项研究表明,使用 DMT 移动应用程序可以在日常生活和临床环境中评估特质和状态冲动的不同方面。DMT 应用程序还可以进一步用于增强我们对冲动行为背后的个体方面的理解,并为数字干预提供有前途的途径。

试验注册

ClinicalTrials.gov NCT03006653; https://www.clinicaltrials.gov/ct2/show/NCT03006653。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/38d7/7837672/e6e14a6e7284/mhealth_v9i1e25018_fig1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验