The DOI matches its Crossref record; 52 of the 55 references it cites were found in a registry or an index.
Return of the JITAI: Applying a Just-in-Time Adaptive Intervention Framework to the Development of m-Health Solutions for Addictive Behaviors
Open accessAbstract
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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“The dose design of app-based interventions has been suggested as an important aspect that profoundly influences intervention effects [13,24,53].”
From Effectiveness and Minimum Effective Dose of App-Based Mobile Health Interventions for Anxiety and Depression Symptom Reduction: Systematic Review and Meta-Analysis (2022) · cites this paper as [24]
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“The use of the JITAI framework to inform intervention design, evaluation, and reporting has potential to enable high-quality evidence for future app-based mHealth interventions for mental illness [13,24].”
From Effectiveness and Minimum Effective Dose of App-Based Mobile Health Interventions for Anxiety and Depression Symptom Reduction: Systematic Review and Meta-Analysis (2022) · cites this paper as [24]
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Ecological momentary assessment
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008) · Annual Review of Clinical Psychology · doi:10.1146/annurev.clinpsy.3.022806.091415 · Cited by 6,454
A study of the behavior of several methods for balancing machine learning training data
Batista, G. E. A. P. A., Prati, R. C., & Monard, M. C. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007735 · Cited by 4,446
Supervised Machine Learning: A Review of Classification Techniques
Kotsiantis, S. B. (2007) · Informatica · Cited by 4,192
A few useful things to know about machine learning
Domingos, P. (2012) · Communications of the ACM · doi:10.1145/2347736.2347755 · Cited by 3,326
Relapse Prevention: Maintenance Strategies in the Treatment of Addictive Behaviors
Washton, A. M. (1986) · Journal of Studies on Alcohol · doi:10.15288/jsa.1986.47.260 · Cited by 2,531
A taxonomy of behavior change techniques used in interventions
Abraham, C., & Michie, S. (2008) · Health Psychology · doi:10.1037/0278-6133.27.3.379 · Cited by 2,525
Eysenbach, G. (2005) · Journal of Medical Internet Research · doi:10.2196/jmir.7.1.e11 · Cited by 2,524
Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018) · Annals of Behavioral Medicine · doi:10.1007/s12160-016-9830-8 · Cited by 2,171
Research works printed but not linked: 2
- Murphy SA. Workshop on Just In Time Adaptive Interventions. Ann Arbor: University of Michigan; 2013. https://community.isr.umich.edu/public/jitai/Workshop.aspx2013 .
- Nahum-Shani I, Smith SN, Tewari A, Witkiewitz K, Collins LM, Spring B, et al. Just in time adaptive interventions (JITAIs): an organizing framework for ongoing health behavior support. Methodol Cent Tech Report. 2014;2014:14–126.
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Metadata
- 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
- Venue
- International Journal of Behavioral Medicine
- Published
- 2017-01-12
- Volume
- 24
- Issue
- 5
- Pages
- 673-682
- ISSN
- 1070-5503 (ISSN-L)
- 1532-7558
- 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
- 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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