Cited by 112Mobile Health and mHealth ApplicationsDigital Mental Health InterventionsSubstance Abuse Treatment and Outcomes

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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

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Goldstein, Stephanie P., Evans, Brittney C., Flack, Daniel, Juarascio, Adrienne, Manasse, Stephanie, Zhang, Fengqing, Forman, Evan M.·2017·International Journal of Behavioral Medicine

DOI 10.1007/s12529-016-9627-yPMC PMC5870794

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Abstract

Purpose 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.Method 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.Results 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.Conclusion 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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Peer Review #1 of "The impact of mental and somatic stressors on physical activity and sedentary behaviour in adults with type 2 diabetes mellitus: a diary study (v0.1)"
(2021) · PeerJ · doi:10.7287/peerj.11579v0.1/reviews/1
Dynamical Outcome Modeling of Fragmented Intensive Longitudinal DataPreprint
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Peer Review #2 of "The impact of mental and somatic stressors on physical activity and sedentary behaviour in adults with type 2 diabetes mellitus: a diary study (v0.2)"
(2021) · PeerJ · doi:10.7287/peerj.11579v0.2/reviews/2
Peer Review #2 of "The impact of mental and somatic stressors on physical activity and sedentary behaviour in adults with type 2 diabetes mellitus: a diary study (v0.1)"
(2021) · PeerJ · doi:10.7287/peerj.11579v0.1/reviews/2

Authors: Goldstein, S. P., Evans, B. C., Flack, D., Juarascio, A., Manasse, S., Zhang, F., & Forman, E. M.
Affiliations: Drexel University (US)
Corresponding author: Goldstein, Stephanie P.
ORCID: Goldstein, Stephanie P.
Evans, Brittney C.
Flack, Daniel
Juarascio, Adrienne
Manasse, Stephanie
Zhang, Fengqing
Forman, Evan M.
Venue: International Journal of Behavioral Medicine
Publisher: Springer Science and Business Media LLC
Published: 2017-01-12
Volume: 24
Issue: 5
Pages: 673-682
ISSN: 1070-5503 (ISSN-L)
1532-7558
DOI: 10.1007/s12529-016-9627-y
PMC: PMC5870794
OpenAlex: W2576300312
Cited by: 112
References: 55
Citations by year: 2026: 11 · 2025: 18 · 2024: 9 · 2023: 9 · 2022: 16 · 2021: 20 · 2020: 7 · 2019: 10 · 2018: 11 · 2017: 1
FWCI: 12.86
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (unspecified)
Locations: International Journal of Behavioral Medicine (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Institute of Mental Health (F31MH108279, K23MH105680)
Topics: Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
Keywords: just-in-time adaptive interventions, mobile health, addictive behavior, relapse prevention, mobile application development, decision rules, coping strategies, user engagement, machine learning
MeSH terms: Adaptation, Psychological, Diet, Humans, Behavior, Addictive, Telemedicine

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