ArticleJMIR research protocols2021
Prospective Prediction of Lapses in Opioid Use Disorder: Protocol for a Personal Sensing Study.
Article in JMIR research protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Forecasting alcohol lapse risk up to two weeks in advance using time-lagged machine learning models.PloS one · 2026Article
- Maximizing Engagement, Trust, and Clinical Benefit of AI-Generated Recovery Support Messages for Alcohol Use Disorder: Protocol for an Optimization Study.JMIR research protocols · 2025Article
- Idiographic Lapse Prediction With State Space Modeling: Algorithm Development and Validation Study.JMIR formative research · 2025Observational
- Machine learning models for temporally precise lapse prediction in alcohol use disorder.Journal of psychopathology and clinical science · 2024Article
- Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study.JMIR mHealth and uHealth · 2023Observational
- Prospective Prediction of Lapses in Opioid Use Disorder: Protocol for a Personal Sensing Study.JMIR research protocols · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundSuccessful long-term recovery from opioid use disorder (OUD) requires continuous lapse risk monitoring and appropriate use and adaptation of recovery-supportive behaviors as lapse risk changes. Available treatments often fail to support long-term recovery by failing to account for the dynamic nature of long-term recovery.
objectiveThe aim of this protocol paper is to describe research that aims to develop a highly contextualized lapse risk prediction model that forecasts the ongoing probability of lapse.
methodsThe participants will include 480 US adults in their first year of recovery from OUD. Participants will report lapses and provide data relevant to lapse risk for a year with a digital therapeutic smartphone app through both self-report and passive personal sensing methods (eg, cellular communications and geolocation). The lapse risk prediction model will be developed using contemporary rigorous machine learning methods that optimize prediction in new data.
resultsThe National Institute of Drug Abuse funded this project (R01DA047315) on July 18, 2019 with a funding period from August 1, 2019 to June 30, 2024. The University of Wisconsin-Madison Health Sciences Institutional Review Board approved this project on July 9, 2019. Pilot enrollment began on April 16, 2021. Full enrollment began in September 2021.
conclusionsThe model that will be developed in this project could support long-term recovery from OUD-for example, by enabling just-in-time interventions within digital therapeutics. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/29563.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.