Clinicians and researchers alike have long believed that stressors play a pivotal etiologic role in risk, maintenance, and/or relapse of alcohol and other substance use disorders (SUDs). Numerous seminal and contemporary theories on SUD etiology posit that stressors may motivate drug use and that individuals who use drugs chronically may display altered responses to stressors. We use foundational basic stress biology research as a lens through which to evaluate critically the available evidence to support these key stress–SUD theses in humans. Additionally, we examine the field's success to date in targeting stressors and stress allostasis in treatments for SUDs. We conclude with our recommendations for how best to advance our understanding of the relationship between stressors and drug use, and we discuss clinical implications for treatment development.

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

Understanding discrepancies in objective performance and subjective reports may aid in the development of effective interventions for nonmedical prescription stimulant use. (Am J Addict 2018;27:238-244).

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

registered reports, increased transparency, and related endeavors [4-6], it will be important to couple carefully the clinically guided hypotheses with appropriate statistical analyses that directly test them.