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Return of the JITAI: Applying a Just-in-Time Adaptive Intervention Framework to the Development of m-Health Solutions for Addictive Behaviors
Journal article   Open access   Peer reviewed

Return of the JITAI: Applying a Just-in-Time Adaptive Intervention Framework to the Development of m-Health Solutions for Addictive Behaviors

Stephanie P. Goldstein, Brittney C. Evans, Daniel Flack, Adrienne Juarascio, Stephanie Manasse, Fengqing Zhang and Evan M. Forman
International journal of behavioral medicine, v 24(5), pp 673-682
01 Oct 2017
DOI: https://doi.org/10.1007/s12529-016-9627-y
PMID: 28083725
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://europepmc.org/articles/pmc5870794View
Accepted (AM),  Open

Abstract

Psychology Psychology, Clinical Social Sciences
Lapses are strong indicators of later relapse among individuals with addictive disorders, and thus are an important intervention target. However, lapse behavior has proven resistant to change due to the complex interplay of lapse triggers that are present in everyday life. It could be possible to prevent lapses before they occur by using m-Health solutions to deliver interventions in real-time. Just-in-time adaptive intervention (JITAI) is an intervention design framework that could be delivered via mobile app to facilitate in-the-moment monitoring of triggers for lapsing, and deliver personalized coping strategies to the user to prevent lapses from occurring. An organized framework is key for successful development of a JITAI. Nahum-Shani and colleagues (2014) set forth six core elements of a JITAI and guidelines for designing each: distal outcomes, proximal outcomes, tailoring variables, decision points, decision rules, and intervention options. The primary aim of this paper is to illustrate the use of this framework as it pertains to developing a JITAI that targets lapse behavior among individuals following a weight control diet. We will detail our approach to various decision points during the development phases, report on preliminary findings where applicable, identify problems that arose during development, and provide recommendations for researchers who are currently undertaking their own JITAI development efforts. Issues such as missing data, the rarity of lapses, advantages/disadvantages of machine learning, and user engagement are discussed.

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81 citations in Web of Science
81 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

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Web of Science research areas
Psychology, Clinical
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