Literature DB >> 33480854

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

Michael Sobolev1,2, Rachel Vitale3, Hongyi Wen1, James Kizer1, Robert Leeman4, J P Pollak1, Amit Baumel5, Nehal P Vadhan2, Deborah Estrin1, Frederick Muench3.   

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. ©Michael Sobolev, Rachel Vitale, Hongyi Wen, James Kizer, Robert Leeman, J P Pollak, Amit Baumel, Nehal P Vadhan, Deborah Estrin, Frederick Muench. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 22.01.2021.

Entities:  

Keywords:  ResearchKit; active task; ecological momentary assessment; impulse control; impulsivity; mHealth; mobile health; self-control; self-regulation

Mesh:

Year:  2021        PMID: 33480854      PMCID: PMC7837672          DOI: 10.2196/25018

Source DB:  PubMed          Journal:  JMIR Mhealth Uhealth        ISSN: 2291-5222            Impact factor:   4.773


  72 in total

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Review 5.  The Genotype and Phenotype (GaP) registry: a living biobank for the analysis of quantitative traits.

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Journal:  Am J Prev Med       Date:  2013-08       Impact factor: 5.043

Review 7.  Self-regulation in childhood as a predictor of future outcomes: A meta-analytic review.

Authors:  Davina A Robson; Mark S Allen; Steven J Howard
Journal:  Psychol Bull       Date:  2020-01-06       Impact factor: 17.737

8.  Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants.

Authors:  Abhishek Pratap; Elias Chaibub Neto; Phil Snyder; Carl Stepnowsky; Noémie Elhadad; Daniel Grant; Matthew H Mohebbi; Sean Mooney; Christine Suver; John Wilbanks; Lara Mangravite; Patrick J Heagerty; Pat Areán; Larsson Omberg
Journal:  NPJ Digit Med       Date:  2020-02-17

9.  Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support.

Authors:  Inbal Nahum-Shani; Shawna N Smith; Bonnie J Spring; Linda M Collins; Katie Witkiewitz; Ambuj Tewari; Susan A Murphy
Journal:  Ann Behav Med       Date:  2018-05-18

Review 10.  What is the clinical value of mHealth for patients?

Authors:  Simon P Rowland; J Edward Fitzgerald; Thomas Holme; John Powell; Alison McGregor
Journal:  NPJ Digit Med       Date:  2020-01-13
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  3 in total

1.  mPulse Mobile Sensing Model for Passive Detection of Impulsive Behavior: Exploratory Prediction Study.

Authors:  Hongyi Wen; Michael Sobolev; Rachel Vitale; James Kizer; J P Pollak; Frederick Muench; Deborah Estrin
Journal:  JMIR Ment Health       Date:  2021-01-27

2.  Digital Prompts to Increase Engagement With the Headspace App and for Stress Regulation Among Parents: Feasibility Study.

Authors:  Lisa Militello; Michael Sobolev; Fabian Okeke; Daniel A Adler; Inbal Nahum-Shani
Journal:  JMIR Form Res       Date:  2022-03-21

3.  Development and evaluation of the HRSD-D, an image-based digital measure of the Hamilton rating scale for depression.

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Journal:  Sci Rep       Date:  2022-08-22       Impact factor: 4.996

  3 in total

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