Background: Successful 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.
Objective: The 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.
Methods: The 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.
Results: The 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.
Conclusions: The 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.
Keywords: digital therapeutics; mobile phone; opioid lapse; risk prediction.
©Hannah Moshontz, Alejandra J Colmenares, Gaylen E Fronk, Sarah J Sant'Ana, Kendra Wyant, Susan E Wanta, Adam Maus, David H Gustafson Jr, Dhavan Shah, John J Curtin. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 07.12.2021.