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
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 DERR1-10.2196/29563
Personal sensing may improve digital therapeutics for mental health care by facilitating early screening, symptom monitoring, risk prediction, and personalized/adaptive interventions. However, further development and use of personal sensing first requires better understanding of its acceptability to people targeted for these mental health applications. We assessed the acceptability of both active and passive personal sensing methods in a sample of people with moderate to severe alcohol use disorder using both behavioral and self-report measures. Participants (N = 154; 50% female; mean age = 41; 87% White, 97% Non-Hispanic) in early recovery (1 – 8 weeks of abstinence) from alcohol use disorder were recruited from the Madison, WI area to participate in a 3-month longitudinal study. Participants engaged with active (ecological momentary assessment; EMA, audio check-in, and sleep quality) and passive (geolocation, cellular communication logs, and text message content) personal sensing methods. We assessed 3 behavioral indicators of acceptability: participants’ choices about their participation in the study at various stages in the study procedure, their choice to opt-in to provide data for each personal sensing method, and their compliance for a subset of the active methods (EMA, audio check-in). We also assessed 3 self-report measures of acceptability (interference, dislike, and willingness to use for 1 year) for each method. All but 1 of the individuals who were eligible to participate consented to the personal sensing procedures. Most of these individuals (88%) also returned 1 week later to formally enroll in the study and begin to provide these data. All participants (100%) opted-in to provide data for EMA, sleep quality, and all passive methods (geolocation, cellular communication logs, text message content). Three participants (2%) did not provide any audio check-ins while on study. The average completion rate for all requested EMAs (4X daily) was 81% for 4x daily and 94% for 1x daily. The completion rate for the daily audio check-in was 55%. Aggregate participant ratings indicated all personal sensing methods to be significantly more acceptable (all P’s < .05) compared to neutral across subjective measures of interference, dislike, and willingness to use for 1 year. Participants did not significantly differ in their dislike of active compared to passive methods (P = .23). However, participants reported a higher willingness to use passive methods for 1 year compared to active methods (P = .04). The results of our study suggest that both active and passive personal sensing methods are generally acceptable to people with alcohol use disorder over a longer period than has previously been assessed. This was true even for data streams that contained potentially more sensitive information (e.g., geolocation, cellular communications). Important individual differences were observed both across people and methods which indicate opportunities for future improvements.
Successful long-term recovery from Opioid Use Disorder requires continuous lapse risk monitoring and appropriately using and adapting 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. This protocol paper describes research that aims to develop a highly contextualized lapse risk prediction model that forecasts the ongoing probability of lapse. Participants will be 480 American adults in their first year of recovery from Opioid Use Disorder. 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 (e.g., cellular communications, geolocation). The lapse risk prediction model will be developed using contemporary rigorous machine learning methods that optimize prediction in new data. The model this project will develop could support long-term recovery from Opioid Use Disorder, for example, by enabling just-in-time interventions within digital therapeutics. This project is funded by the National Institute on Drug Abuse with a funding period from August 2019 to June 2024. Full enrollment began in September 2021.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.